diff --git a/scripts/combine54.py b/scripts/combine54.py
new file mode 100644
index 0000000..a29ff5b
--- /dev/null
+++ b/scripts/combine54.py
@@ -0,0 +1,67 @@
+import pandas as pd
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+curdir = os.getcwd()
+path_delim = '/'
+df1 = pd.read_excel(curdir + path_delim + "data/users_05_12_2023_19_13.xlsx", sheet_name="Sheet1")
+df2 = pd.read_excel(curdir + path_delim + "data/tests_05_12_2023_18_27.xlsx", sheet_name="data")
+
+df2 = df2.sort_values('testTime')
+
+variance_column = df2["led2Buffer"].var(ddof=0)
+print(variance_column)
+
+# Extract rows from df1 where 'bloodGroup' is present for duplicated entries
+duplicated_ids = df1[df1.duplicated(subset=['_id'], keep=False)]['_id']
+df1_with_bloodgroup = df1[df1['_id'].isin(duplicated_ids) & df1['bloodGroup'].notna()]
+
+# Merge the relevant rows back into df2
+df2 = df2.merge(df1_with_bloodgroup, on='_id', how='left', suffixes=('', '_df1'))
+
+df = df2.merge(df1, on="_id", how="inner")
+print(df)
+# # df = pd.concat([df1, df3], ignore_index=True)
+# df['Age'] = 2023 - df['birthYear']
+
+# df['deviceId'].hist()
+# # plt.show()
+
+# df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "classificationResult", "bloodGroup", "testTime", "caste", "registrationCenterName"]]
+# 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", "testTime": "Date", "classificationResult": "Test Result", "bloodGroup": "Blood Group", "Age": "Age", "caste": "Caste", "registrationCenterName": "Center"}, 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", "Center"], axis=1)
+
+# # df['Date'] = pd.to_datetime(df["Date"].dt.strftime('%d-%m-%Y'))
+
+# # df = df.sort_values(by=['Date'], ascending=True)
+
+# df["Test Result"].fillna("NOTEST", inplace = True)
+# print("NOTEST: ", len(df[df["Test Result"] == "NOTEST"]))
+# df = df[df["Test Result"] != "NOTEST"]
+
+# df["Blood Group"].fillna("NOBLOODGROUP", inplace = True)
+# print("NO BLOOD GROUP: ", len(df[df["Blood Group"] == "NOBLOODGROUP"]))
+
+# df_final = df.sort_values('Date').drop_duplicates('Sample ID', keep='last')
+
+# 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'] == 'Sickle Cell Disease', 'Test Result'] = 'Sickle Cell Disease (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())
+
+# df_count = df.groupby(["Test Result"]).describe()["ABHA ID"]["count"]
+# print(df.groupby(["Test Result"]).describe()["ABHA ID"]["count"])
+
+writer = pd.ExcelWriter(curdir + path_delim + "data/Dec05a.xlsx", engine = 'openpyxl')
+df.to_excel(writer, sheet_name = 'op', index=False)
+# df_count.to_excel(writer, sheet_name = "count")
+writer.close()
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