diff --git a/cloud-functions/python/functions/consolidation/requirements.txt b/cloud-functions/python/functions/consolidation/requirements.txt
new file mode 100644
index 0000000..0a87d59
--- /dev/null
+++ b/cloud-functions/python/functions/consolidation/requirements.txt
@@ -0,0 +1,6 @@
+functions-framework==3.*
+firebase_functions~=0.1.0
+pandas==2.0.3
+openpyxl==3.1.2
+firebase-admin==6.2.0
+google-cloud-firestore==2.14.0
\ No newline at end of file
diff --git a/scripts/consolidated_data.py b/scripts/consolidated_data.py
index f24b551..d759b83 100644
--- a/scripts/consolidated_data.py
+++ b/scripts/consolidated_data.py
@@ -9,7 +9,7 @@ import sys
 import platform
 from datetime import datetime, timedelta
 
-environment = "af3cc"
+environment = "qa"
 
 if __name__ == "__main__":
 
@@ -67,11 +67,22 @@ if __name__ == "__main__":
         data.append(test_data)
 
     # Convert the data to a DataFrame
-    df = pd.DataFrame(data)
-    print(df)
+    test_df = pd.DataFrame(data)
+    print(test_df)
+    print(test_df.columns)
+
+    query = db.collection("patientData").where(filter=FieldFilter("createdAt", ">=", start_date)).where(filter=FieldFilter("createdAt", "<", end_date))
+    data = []
+    docs = query.stream()
+    for doc in docs:
+        row_data = doc.to_dict()
+        data.append(row_data)
+
+    user_df = pd.DataFrame(data)
+
+    df = pd.merge(test_df, user_df, on='_id')
     print(df.columns)
-
-
+    
     # Save the DataFrame to a CSV file
     output_filename = f'data_{environment}_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
     df.to_csv(output_filename, index=False)
@@ -82,7 +93,7 @@ if __name__ == "__main__":
     df = df[(df['testTime'] > start_date) & (df['testTime'] <= end_date)]
     df = df.sort_values(by=['testTime'], ascending=False)
 
-    column_names = ["_id", "classificationResult", "deviceRatioClass",  "prdClassification", "slopeRatioClass", "predictedDenovixRatio", "slopeRatio", "calculatedRatio", "deviceRatio", "kitSerial", "led1Gain1", "led1Gain2", "led1Gain4", "abs1", "led1Average", "led1Buffer", "led1Sample", "led2Gain1", "led2Gain2", "led2Gain4", "abs2", "led2Average", "led2Buffer", "led2Sample", "led3Gain1", "led3Gain2", "led3Gain4", "hb3", "abs3", "led3Average", "led3Buffer", "led3Sample", "led4Gain1", "led4Gain2", "led4Gain4", "hb4", "abs4", "led4Average", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "solution", "concentration", "volume", "errorMessages", "deviceId", "deviceSerialNumber", "appVersion", "name", "testTime"]
+    column_names = ["_id", "classificationResult", "deviceRatioClass",  "prdClassification", "slopeRatioClass", "predictedDenovixRatio", "slopeRatio", "calculatedRatio", "deviceRatio", "kitSerial", "led1Gain1", "led1Gain2", "led1Gain4", "abs1", "led1Average", "led1Buffer", "led1Sample", "led2Gain1", "led2Gain2", "led2Gain4", "abs2", "led2Average", "led2Buffer", "led2Sample", "led3Gain1", "led3Gain2", "led3Gain4", "hb3", "abs3", "led3Average", "led3Buffer", "led3Sample", "led4Gain1", "led4Gain2", "led4Gain4", "hb4", "abs4", "led4Average", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "solution", "concentration", "volume", "errorMessages", "deviceId", "deviceSerialNumber", "appVersion", "name_y", "testTime"]
     common_columns = [col for col in column_names if col in df.columns]
     df = df[common_columns]