Merge, Identify Duplicates, report duplicates with all details

@glanegonsalves

Hey there Glane here, hope you're doing well. I can help you data manipulation and creating a single consolidated sheet using pandas. Feel free to get in touch

@zafarfarooq612

Hello, I will process 65 lakh voter records across 10,000 Excel files within 4 hours using Python. I will consolidate all data, find duplicates based on Name+Relative+Age+Gender+House Number, tag Source/Copy records, add door‑number‑free version, count flags, and split back into original folder structure with no data loss. Deliverables: master Excel, regenerated folders, flag report. Ready to start immediately. Regards, Zafar

@vsprybhanu

Hi, I can merge all 10,000 voter list Excel files (approx. 65 lakh records) into one master workbook, perform exact duplicate matching using Name + Relative Name + Age + Gender + House No. (including Door No.), and generate clearly tagged “Source” and “Copy” records with all required fields, including Assembly details and EPIC IDs. I will also count flag varieties and regenerate part/assembly-wise Excel files back into the same folder structure with zero data loss. I can complete this urgent task within 4 hours and deliver clean, final .xlsx outputs exactly as required. Best Regards, Virendra

@shahajalalbabu00

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@mdabu38

I will efficiently merge all 10,000 voter Excel files, detect exact duplicates using your criteria, generate a clean master file, and recreate perfectly structured part-wise outputs with zero data loss within your deadline.

@Hemantt841

Handling a dataset of this magnitude—approximately 6.5 million records—requires a precise and efficient approach to ensure data integrity while performing complex duplicate detection based on multiple key fields. The challenge lies not only in merging thousands of voter lists scattered across numerous folders but also in accurately tagging duplicates with comprehensive details and then re-segregating the data into their original folder structures without any loss. This task demands meticulous data wrangling and robust processing to deliver a seamless master workbook and correctly partitioned output files. Leveraging advanced data processing capabilities in Python, particularly with pandas for efficient handling of large Excel datasets, will facilitate the exact-match duplicate detection on composite keys including Name, Relative Name, Age, Gender, House Number, and Assembly Name. The process will involve consolidating all source files into a master dataframe, implementing a reliable duplicate tagging system that generates paired “Source” and “Copy” records with all required fields, and then slicing the master data back into partitioned Excel files that mirror the original folder hierarchy. The use of optimized I/O operations and memory management will ensure timely completion within the tight four-hour window. Commitment to accuracy and completeness will be paramount, guaranteeing that no data is lost and that all duplicates are correctly identified and reported with full transparency. The final deliverables will be fully compliant with the specified format, enabling easy verification and further analysis. Ready to start immediately and deliver a comprehensive, error-free solution within the deadline—please share access details so the process can begin without delay

@GobindSawarn

This cannot be done reliably in 4 hours manually—65 lakh (~6.5M) records across 10,000 files requires automation and batching. The only practical way is to process it using Python + pandas and then deliver the final Excel outputs. How I’ll fix it (fast & accurate): Bulk Read & Merge Read all “voter list” Excel files from Google Drive folders Add metadata columns: Part No, Assembly No, Assembly Name Append into one master dataset (chunk-wise to avoid memory crash) Duplicate Detection (Exact Match) Key: Name + Relative Name + Age + Gender + House No Use hashing/groupby for high-speed matching Mark duplicates and create paired rows (Source + Copy) Output Structure Add required fields: Serial No, Part No, Assembly details EPIC IDs (both source & copy) Two versions (with & without house no) Count and summarise flag column distribution Split Back to Files Recreate part-wise / assembly-wise Excel files Maintain same folder structure Ensure no data loss (row validation check) Reality check (important): Processing 6.5M rows + Excel writing = 2–4 hours minimum even with optimized scripts Google Drive download/upload itself will take time Excel has row limits → may require splitting or using optimized writing Best approach to meet urgency: Run script on high-RAM system (16–32GB) Parallel processing (multi-core) Deliver: Master file (or chunked) Split files ready in folders Flag summary report Regard Govind

@Sadiyasyeda24

I saw your project and am confident I can deliver on this. I'm currently working on a similar project and will merge all voter list Excel files, identify duplicates based on Name, Relative Name, Age, Gender, and House Number, and create detailed records for each duplicate. By analyzing the project details, I assure you that I can efficiently complete this task, ensuring the required benefit of a consolidated Excel workbook with accurate duplicate pairs and detailed records for each entry. I invite you to view my portfolio, which showcases the quality and results of my past work. I look forward to hearing from you. Regards, Sadiya