146 lines
13 KiB
Python
146 lines
13 KiB
Python
import pandas as pd
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import os
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curdir = os.getcwd()
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path_delim = '/'
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df1 = pd.read_csv(curdir + path_delim + "data/all_users_14_07_2023_to_12_09_2023.csv")
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df2 = pd.read_csv(curdir + path_delim + "data/tests_14_07_2023_to_12_09_2023.csv")
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df4 = pd.read_excel(curdir + path_delim + "data/Jul 26_30 2023_FinalDeclare.xlsx", sheet_name="results")
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df5 = pd.read_excel(curdir + path_delim + "data/Jul31-Aug5 2023_FinalDeclare.xlsx", sheet_name="Result")
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df_sept1 = pd.read_excel(curdir + path_delim + "data/NagpurData_FinalResults_Aug1 Shared.xlsx", sheet_name="Sheet1")
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df_sept3 = pd.read_excel(curdir + path_delim + "data/Sept 3 Final Result Declared.xlsx", sheet_name="Sheet1")
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df_sept4 = pd.read_excel(curdir + path_delim + "data/Final Results Sept 4.xlsx", sheet_name="Sheet1")
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df_sept5 = pd.read_excel(curdir + path_delim + "data/September 5 Final Results.xlsx", sheet_name="Sheet1")
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df_sept6 = pd.read_excel(curdir + path_delim + "data/FinalResults 6 Sept.xlsx", sheet_name="Sheet3")
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df_sept7 = pd.read_excel(curdir + path_delim + "data/Final Results 07-Sept.xlsx", sheet_name="Sheet1")
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df_sept8 = pd.read_excel(curdir + path_delim + "data/FinalResults 8 Sept.xlsx", sheet_name="Sheet1")
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df6 = pd.read_excel(curdir + path_delim + "data/FinalResults - 9 Sept. 2023.xlsx", sheet_name="Sheet1")
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df7 = pd.read_excel(curdir + path_delim + "data/FinalResults - 11 Sept 2023.xlsx", sheet_name="Sheet3")
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df8 = pd.read_excel(curdir + path_delim + "data/FinalResults Sept 12-13, 2023.xlsx", sheet_name="Sheet1")
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### merging all user data for master
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df = df1.merge(df2, on="_id")
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# df = df.merge(df4, on="_id")
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df['Age'] = 2023 - df['birthYear_x']
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# print(df.columns)
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# print(df)
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master_df = df
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### merging Jul 26 to 30
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df = master_df.merge(df4, on="aadharId")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "Test Result", "bloodGroup", "testTime"]]
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# df = df.sort_values(by=['testTime'], ascending=True)
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df.rename(columns={'_id': "Sample ID", "name": "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": "Test Time", "Test Result": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name_x": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_1 = df
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### merging July 31 - Aug 5
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df = master_df.merge(df5, on="aadharId")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "Test Result", "bloodGroup", "testTime"]]
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df.rename(columns={'_id': "Sample ID", "name": "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": "Test Time", "Test Result": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name_x": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_2 = df
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### merging Sept 1
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df_result_sept1 = df_sept1
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### merging Sept 3
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df = master_df.merge(df_sept3, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "Test Result", "bloodGroup", "testTime"]]
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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": "Test Time", "bloodGroup": "Blood Group", "Age_x": "Age", "name": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_sept3 = df
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### merging Sept 4
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df = master_df.merge(df_sept4, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "HPLC Results", "bloodGroup", "testTime"]]
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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": "Test Time", "HPLC Results": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_sept4 = df
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### merging Sept 5
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df = master_df.merge(df_sept5, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "HPLC Results", "bloodGroup", "testTime"]]
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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": "Test Time", "HPLC Results": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_sept5 = df
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### merging Sept 6
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df = master_df.merge(df_sept6, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "Classification", "bloodGroup", "testTime"]]
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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": "Test Time", "Classification": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_sept6 = df
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### merging Sept 7
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df = master_df.merge(df_sept7, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "HPLC", "bloodGroup", "testTime"]]
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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": "Test Time", "HPLC": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_sept7 = df
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### merging Sept 8
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df = master_df.merge(df_sept8, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age_x", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "HPLC", "bloodGroup", "testTime"]]
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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": "Test Time", "HPLC": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_sept8 = df
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### merging Sept 9
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df = master_df.merge(df6, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "Denovix Classification", "bloodGroup", "testTime"]]
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df.rename(columns={'_id': "Sample ID", "name": "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": "Test Time", "Denovix Classification": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name_x": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_3 = df
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### merging Sept 11
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df = master_df.merge(df7, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "classification-Estimated Denovix", "bloodGroup", "testTime"]]
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df.rename(columns={'_id': "Sample ID", "name": "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": "Test Time", "classification-Estimated Denovix": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name_x": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_4 = df
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### merging Sept 12, 13
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df = master_df.merge(df8, on="_id")
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "classification-Estimated Denovix", "bloodGroup", "testTime"]]
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df.rename(columns={'_id': "Sample ID", "name": "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": "Test Time", "classification-Estimated Denovix": "Test Result", "bloodGroup": "Blood Group", "Age_x": "Age", "name_x": "Name"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Test Time", "Test Result", "Blood Group"], axis=1)
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df_result_5 = df
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### append all results
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df_final = pd.concat([df_result_1, df_result_2, df_result_sept1, df_result_sept3, df_result_sept5, df_result_sept6, df_result_sept7, df_result_sept8, df_result_3, df_result_4, df_result_5])
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df_final.loc[df_final['Test Result'] == 'Normal', 'Test Result'] = 'Normal (HbA)'
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df_final.loc[df_final['Test Result'] == 'Sickle Cell Trait', 'Test Result'] = 'Sickle Cell Trait (HbAS)'
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df_final.loc[df_final['Test Result'] == 'SCT', 'Test Result'] = 'Sickle Cell Trait (HbAS)'
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df_final.loc[df_final['Test Result'] == 'SCD', 'Test Result'] = 'Sickle cell Disease (HbSS)'
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df_final.loc[df_final['Test Result'] == 'PBL', 'Test Result'] = 'Positive Borderline'
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df_final.loc[df_final['Test Result'] == 'NBL', 'Test Result'] = 'Negative Borderline'
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df_final.loc[df_final['Gender'] == 'Male', 'Gender'] = 'MALE'
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df_final.loc[df_final['Gender'] == 'Female', 'Gender'] = 'FEMALE'
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#df_final = df.drop_duplicates(subset=['Sample ID', "Test Time"], keep='last')
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df_dedup = pd.read_excel(curdir + path_delim + "data/July_Sept_12_shared.xlsx", sheet_name="op")
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print(df_dedup)
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print(df_dedup.groupby(["Gender"]).describe())
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writer = pd.ExcelWriter(curdir + path_delim + "data/July_Sept.xlsx", engine = 'openpyxl')
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df_final.to_excel(writer, sheet_name = 'op', index=False)
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writer.close()
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