diff --git a/pyproject.toml b/pyproject.toml
index 79695c1..25ef46d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "dq-suite-amsterdam"
-version = "0.0.9"
+version = "0.0.10"
 authors = [
   { name="Arthur Kordes", email="a.kordes@amsterdam.nl" },
 ]
diff --git a/requirements.txt b/requirements.txt
index c88eead..5fc23cf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1,2 @@
-great_expectations
\ No newline at end of file
+great_expectations
+pandas
\ No newline at end of file
diff --git a/src/dq_suite/__init__.py b/src/dq_suite/__init__.py
index 9571006..381c95f 100644
--- a/src/dq_suite/__init__.py
+++ b/src/dq_suite/__init__.py
@@ -2,6 +2,10 @@
 
 from dq_suite.input_validator import validate_dqrules
 from dq_suite.df_checker import df_check
+from dq_suite.output_transformations import (
+    extract_dq_validatie_data,
+    extract_dq_afwijking_data
+)
 
 # Use __all__ to let developers know what is part of the public API.
 __all__ = [
diff --git a/src/dq_suite/df_checker.py b/src/dq_suite/df_checker.py
index 228c199..1065607 100644
--- a/src/dq_suite/df_checker.py
+++ b/src/dq_suite/df_checker.py
@@ -9,6 +9,8 @@
 from great_expectations.checkpoint import Checkpoint
 
 from dq_suite.input_validator import validate_dqrules
+from dq_suite.output_transformations import extract_dq_validatie_data
+from dq_suite.output_transformations import extract_dq_afwijking_data
 
 
 def df_check(df: DataFrame, dq_rules: str, check_name: str) -> str:
@@ -21,8 +23,8 @@ def df_check(df: DataFrame, dq_rules: str, check_name: str) -> str:
     :type dq_rules: str
     :param check_name: Name of the run for reference purposes
     :type check_name: str
-    :return: A JSON string with the DQ results, parsed from the GX output
-    :rtype: str.
+    :return: Two tables df result_dqValidatie - result_dqAfwijking with the DQ results, parsed from the GX output
+    :rtype: df.
     """
     name = check_name
     validate_dqrules(dq_rules)
@@ -83,7 +85,10 @@ def df_check(df: DataFrame, dq_rules: str, check_name: str) -> str:
 
     # Parse output
     output = checkpoint_result["run_results"]
+    
     for key in output.keys():
         result = output[key]["validation_result"]
+        result_dqValidatie = extract_dq_validatie_data(name,result)
+        result_dqAfwijking = extract_dq_afwijking_data(name,result)
 
-    return result
+    return result_dqValidatie, result_dqAfwijking
diff --git a/src/dq_suite/output_transformations.py b/src/dq_suite/output_transformations.py
new file mode 100644
index 0000000..f50cd80
--- /dev/null
+++ b/src/dq_suite/output_transformations.py
@@ -0,0 +1,70 @@
+import pandas as pd
+
+
+def extract_dq_validatie_data(check_name, dq_result):
+    """Function takes a json with the GX output and a string check_name and returns dataframe.
+    
+    :param df_dq_validatie: A df containing the valid result
+    :type df: DataFrame
+    :param dq_rules: A JSON string containing the Data Quality rules to be evaluated
+    :type dq_rules: str
+    :param check_name: Name of the run for reference purposes
+    :type check_name: str
+    :return: A table df with the valid result DQ results, parsed from the extract_dq_validatie_data output
+    :rtype: df.
+    """
+
+    # Access run_time attribute
+    run_time = dq_result["meta"]["run_id"].run_time
+    # Extracted data
+    extracted_data = []
+    for result in dq_result["results"]:
+        element_count = int(result["result"].get("element_count", 0))
+        unexpected_count = int(result["result"].get("unexpected_count", 0))
+        aantal_valide_records = element_count - unexpected_count
+        expectation_type = result["expectation_config"]["expectation_type"]
+        attribute = result["expectation_config"]["kwargs"].get("column")
+        dq_regel_id = f"{check_name}_{expectation_type}_{attribute}"
+        extracted_data.append({
+            "dqRegelId": dq_regel_id,
+            "aantalValideRecords": aantal_valide_records,
+            "aantalReferentieRecords": element_count,
+            "dqDatum": run_time,
+        })
+    # Create a DataFrame
+    df_dq_validatie = pd.DataFrame(extracted_data)
+    return df_dq_validatie
+
+
+def extract_dq_afwijking_data(check_name, dq_result):
+    """
+    Function takes a json dq_rules,and a string check_name and returns dataframe.
+    
+    :param df_dq_validatie: A df containing the invalid(deviated) result
+    :type df: DataFrame
+    :param dq_rules: A JSON string containing the Data Quality rules to be evaluated
+    :type dq_rules: str
+    :param check_name: Name of the run for reference purposes
+    :type check_name: str
+    :return: A table df with the invalid result DQ results, parsed from the extract_dq_afwijking_data output
+    :rtype: df.
+    """
+    # Extracting information from the JSON
+    run_time = dq_result["meta"]["run_id"].run_time  # Access run_time attribute
+    # Extracted data
+    extracted_data = []
+    for result in dq_result["results"]:
+        filter_veld_waarde = result["expectation_config"]["kwargs"].get("column")
+        expectation_type = result["expectation_config"]["expectation_type"]
+        attribute = result["expectation_config"]["kwargs"].get("column")
+        dq_regel_id = f"{check_name}_{expectation_type}_{attribute}"
+        afwijkende_attribuut_waarde = result.get("result", {}).get("partial_unexpected_list", [])
+        for value in afwijkende_attribuut_waarde:
+            extracted_data.append({
+                "dqRegelId": dq_regel_id,
+                "afwijkendeAttribuutWaarde": value,
+                "dqDatum": run_time,
+            })
+    # Create a DataFrame
+    df_dq_afwijking = pd.DataFrame(extracted_data)
+    return df_dq_afwijking