diff --git a/scripts/combine9.py b/scripts/combine9.py
new file mode 100644
index 0000000..cc3f926
--- /dev/null
+++ b/scripts/combine9.py
@@ -0,0 +1,39 @@
+import pandas as pd
+import os
+import numpy as np
+
+curdir = os.getcwd()
+path_delim = '/'
+df1 = pd.read_csv(curdir + path_delim + "data/users_23_09_2023_13_41.csv")
+df2 = pd.read_excel(curdir + path_delim + "data/tests_23_09_2023_13_41.xlsx", sheet_name="data")
+
+
+df = df1.merge(df2, on="_id", how='outer')
+# print(df.columns)
+# # df = pd.concat([df1, df3], ignore_index=True)
+df['Age'] = 2023 - df['birthYear']
+
+
+df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "classificationResult", "bloodGroup", "createdAt", "caste"]]
+print(df)
+
+df.rename(columns={'_id': "Sample ID", "name_x": "Name", "abhaId": "ABHA ID", "aadharId": "Aadhaar ID", "gender": "Gender", "category": "Category", "maritalStatus": "Marital Status", "house": "Address", "district": "District", "state": "State", "pinCode": "Pincode", "phoneNumber": "Mobile Number", "createdAt": "Date", "classificationResult": "Test Result", "bloodGroup": "Blood Group", "Age": "Age", "caste": "Caste"}, inplace = True)
+df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Caste", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Date", "Test Result", "Blood Group"], axis=1)
+# df['Date'] = pd.to_datetime(df["Date"].dt.strftime('%d-%m-%Y'))
+
+# df = df.sort_values(by=['Date'], ascending=True)
+df_final = df
+
+df_final.loc[df_final['Test Result'] == 'Normal', 'Test Result'] = 'Normal (HbA)'
+df_final.loc[df_final['Test Result'] == 'Sickle Cell Trait', 'Test Result'] = 'Sickle Cell Trait (HbAS)'
+df_final.loc[df_final['Test Result'] == 'SCT', 'Test Result'] = 'Sickle Cell Trait (HbAS)'
+df_final.loc[df_final['Test Result'] == 'SCD', 'Test Result'] = 'Sickle cell Disease (HbSS)'
+df_final.loc[df_final['Test Result'] == 'PBL', 'Test Result'] = 'Positive Borderline'
+df_final.loc[df_final['Test Result'] == 'NBL', 'Test Result'] = 'Negative Borderline'
+df_final.loc[df_final['Gender'] == 'Male', 'Gender'] = 'MALE'
+df_final.loc[df_final['Gender'] == 'Female', 'Gender'] = 'FEMALE'
+print(df_final.groupby(["Test Result"]).describe())
+
+writer = pd.ExcelWriter(curdir + path_delim + "data/Sept23.xlsx", engine = 'openpyxl')
+df_final.to_excel(writer, sheet_name = 'op', index=False)
+writer.close()