Clean & Reformat CSV/Excel

@vano101

Hello! I can clean and standardize your CSV/Excel files using Python/pandas, with a focus on accuracy and reproducibility. I’ll remove duplicate rows, identify and handle missing values, standardize dates/numbers/text formats, and validate the final dataset for consistency. I’ll return both a clean `.xlsx` workbook and matching `.csv` export, plus a short README documenting any assumptions or edge cases. I’m experienced with large spreadsheet/data-cleaning tasks and can start immediately.

@glanegonsalves

Hi, Glane here. I can handle the complete CSV or Excel data cleaning workflow using Python via pandas, including removing duplicate rows, identifying and appropriately treating missing values, standardizing dates, numbers, text, and other column formats, and documenting all conventions used. I’ll also flag any unusual edge cases in a short README so the process remains transparent and reproducible. You’ll receive both a cleaned .xlsx workbook and matching .csv file, ready to use directly in your analysis pipeline.

@belarjsoufiane20

I can handle this efficiently using Python/pandas and Excel, with a reproducible cleaning workflow. I have extensive experience in data cleaning, validation, and statistical analysis, backed by a Master’s in Statistics and 7+ years of data-analysis experience. I’ll remove exact duplicates, systematically audit missing values, standardize dates/numeric/text fields based on their actual data types, and run final consistency checks to ensure the cleaning itself hasn’t introduced errors. Any ambiguous missing values or unusual records will be flagged rather than guessed. You’ll receive a clean XLSX and matching CSV, plus a brief summary of duplicates removed, missing-value treatment, formatting changes, and any edge cases requiring attention. The process will be scripted so the results are fully reproducible.

@bktk

Hi, I can clean and standardize your CSV/Excel files . I’ll remove duplicates, handle missing values, normalize dates/numbers/text formats, and carefully check for edge cases. I’ll deliver both clean .xlsx and .csv files along with a short README documenting any special handling. I can ensure the process is accurate, consistent, and reproducible.

@gustavoguanilo

Hi! I'll clean your data with a reproducible Python/pandas script, so you can re-run it on future files with one command. WHAT YOU GET - Duplicates removed: exact matches plus near-duplicates (extra spaces, different letter case), each reviewed before removal - Missing values: logical defaults only where obvious, otherwise a clear placeholder such as "MISSING" - Consistent formats: dates (YYYY-MM-DD), numbers, currency, phone numbers, text case and trimmed spaces, with clean headers - Ambiguous entries are never silently changed: they go to a "Flagged" sheet with the reason, for your decision - Output: a formatted .xlsx workbook and a matching .csv (UTF-8) - README: steps, rules applied, edge cases, and a before/after summary (rows removed, values filled) PLAN 1. Profile the file and send you a short issues report 2. Clean it, then you review the flagged items 3. Final delivery in 2-3 days after receiving the file I'm a Senior Engineer with 13 years on Freelancer, working daily with Python, databases and data automation. Quick question: how many rows and columns does the file have, and is it one sheet or several? Best regards,

@muneeb6767

Hey, this is exactly the kind of cleanup job I do regularly — dedup, missing-value handling, and standardizing formats across messy CSV/Excel exports before they go into a pipeline. I'd start by profiling each file (row counts, null %, format inconsistencies per column) before touching anything, so nothing gets silently changed without a clear rule behind it. Duplicates removed on full-row match (or a key you specify), missing values filled with logical defaults where safe (mode/median/etc.) or flagged with a clear placeholder like MISSING_VALUE otherwise, dates/numbers/text normalized to one consistent style, and everything documented in a short read-me covering any edge cases I hit. Output delivered as both .xlsx and .csv, matching row-for-row. I've built large-scale data pipelines (real estate listings, product catalogs) feeding databases like Supabase, so clean, analysis-ready output is something I pay close attention to. Quick questions: roughly how many files/rows are we talking about, and is there a natural key column I should treat duplicates against, or go by full-row matches? Can turn this around fast — happy to start as soon as you share the file(s).

@ASSAMWEB

Greetings, I have reviewed your project description and recently worked on a similar project: ”Stock Market Data”. I believe I can help you deliver this successfully. Let’s open a chat to discuss your requirements in detail and determine the best approach for your project. Regards

@LordAskar

You need duplicate removal, missing value handling with clear labels or logical defaults, and uniform column formatting across both .xlsx and .csv exports ready for your downstream pipeline. To ensure reproducibility and avoid data corruption from mixed date types or text anomalies, I will handle the cleanup using Python and pandas. In a previous Python data formatting project, I extracted, processed, and structured data into clean Excel and CSV outputs. My plan is to inspect the raw schema, remove duplicates, normalize dates and numbers into consistent formats, treat missing entries with documented defaults, and generate both .xlsx and .csv exports alongside a short read-me covering any edge cases. I work through pre-agreed milestones. Do you have a preferred convention for date formats (such as ISO YYYY-MM-DD) and missing text placeholders, or should I standardize on ISO defaults? Ahmed

@SamirBanna

I am very interested in applying for your job since it seems to fit very will with my experience and skills. Regards SamirBanna

@om26er

As a seasoned developer with deep expertise in Python and data management, I am well-equipped to give your CSV/Excel files the thorough cleanup they need. Throughout my career, I have been extensively involved in managing and organizing complex datasets, and your project aligns perfectly with my skills. With an end-to-end approach to projects, I will not only remove duplicates and treat missing values but also ensure all your columns adhere to a consistent, easy-to-read format for seamless analysis. Using my solid command over Python tools such as pandas, I guarantee utmost accuracy and reproducibility in the results. Moreover, working with me means getting meticulous documentation throughout the process. Feel confident that any edge cases that arise will not only be treated but also reported alongside necessary steps taken — allowing you to stay fully informed on every change made. Leverage my experience today for your streamlined data pipeline tomorrow. Let's create concise and well-formatted outputs - both .xlsx and .csv formats - so you can implement them directly into downstream processes without any hiccups! Lets chat

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@shwetadinkar

Hello, I can deliver a reproducible Python/pandas cleanup workflow plus cleaned .xlsx and matching UTF-8 CSV outputs. I will profile the schema, remove exact duplicate rows and report the count, standardize headers and text whitespace, validate/coerce date and numeric fields, and apply consistent formatting. Where no safe logical default is evident, I will use an explicit MISSING placeholder rather than silently infer data. The handover will include a short README/data dictionary covering conventions, transformations, duplicate and missing-value handling, row counts, and any edge cases. Before delivery, I will validate headers, row counts, duplicate removal, missing-value labels, and CSV/XLSX parseability. Assumption: files are readable, non-password-protected CSV/XLSX files; business-specific defaults and leading-zero fields can be confirmed after award. Relevant overlap: I built an .xlsx-ingestion utility with robust column handling: https://www.freelancer.com/portfolio-items/11468743-browser-form-filler-fully-automat I also produced a structured 43-sheet Excel workbook; the overlap is workbook organization and documentation: https://www.freelancer.com/portfolio-items/11566396-broker-competitive-intelligence Regards, Shweta

@rimiloca

I can clean the files with a reproducible pandas workflow, preserving untouched originals and producing matching XLSX/CSV outputs. I will define duplicate rules first, standardise dates/numbers/text without losing IDs or leading zeros, and handle missing values only through agreed defaults or explicit placeholders. The delivery will include row-count reconciliation, a before/after summary and a README of ambiguous cases and conventions. This quote covers up to five files, 100,000 total rows and 50 columns sharing one schema. Do duplicates mean identical rows or matching key fields, and is there an expected date locale?

@andrey13771

Greetings! I am a proficient python developer and data scientist. I've lots of experience in working with tabular data, processing, formatting and anything else you need to do with it. Message me and we can discuss your project! Sincerely, Andrii

@themsyterious03

Hello, I can clean your CSV/Excel data and deliver matching XLSX and CSV files, together with a concise readme documenting the cleaning rules and any exceptions. My approach includes removing duplicate rows, handling missing values using agreed defaults or clearly identified placeholders, and standardizing dates, numbers, and text while preserving identifiers and leading zeros. I’ll validate row counts and check that both output formats contain consistent data. My ₹7,000 bid covers up to 3 flat data tables, totaling 50,000 rows and up to 30 columns per table, plus one revision within the agreed cleaning rules. Delivery is within 7 days of receiving the complete files and clarifications. External data research and manual reconstruction of missing information are excluded. Please confirm the approximate data volume and whether duplicates should be matched across entire rows or specific key columns. If your files exceed the stated scope, we can agree on revised terms before starting. Best regards, Ahmed

@caajoys

I can turn your raw CSV/Excel files into clean, consistent, analysis-ready datasets with a reproducible process and clear documentation. My focus will be on preserving your original information while removing duplicates, resolving data-quality issues, and standardizing every field. Thank you for considering my proposal. I have gone through the requirements in detail. I’ll identify and remove duplicate records, review missing values, apply logical defaults only where clearly appropriate, and flag anything requiring attention. Dates, numbers, text, and other fields will be standardized consistently across all files. I can use Python/pandas or Power Query depending on the dataset and will document the rules applied so the cleanup can be repeated reliably. I have uploaded samples similar to this project completed by me earlier in my profile. With 10+ years of experience, Chartered Accountant (ICAI) and CPA, I bring strong Excel, data cleaning, reconciliation, and quality-control expertise. I’ll deliver matching .xlsx and .csv files, along with a concise read-me covering special cases and cleanup rules. Payment & delivery assurance: ✅ No upfront payment ✅ Release payment after completion or milestone ✅ Regular update related to project ✅ Timely delivery ✅ 100% commitment to project completion Please share the files and I can begin with a quick quality check before processing the full dataset.

@robertb297

Two questions that decide how the cleanup is done: what counts as a duplicate in your file (the whole row, or the same ID or email with other columns different), and do you know where the dates come from? Day-first versus month-first dates are the most common silent error in files like this, so I check them against the values that can only be one or the other, such as a 25 in the day position, before converting anything. How I handle it: strip stray spaces and fix casing first, since "ACME " and "Acme" are otherwise counted as two different rows. Then remove exact duplicates and list near-duplicates separately for you to confirm instead of deleting them. Missing values get a clear placeholder or a defensible default, marked in a separate column, never filled in quietly. You get the .xlsx and a matching .csv, plus a short change log: rows before and after, how many duplicates were removed, how many cells were filled or flagged, and the convention used for each column, so you can check every change. Send the file whenever you are ready. I can turn a first pass around within a day.

@mahnoorb78

Hello! I'm Mahnoor, an experienced and meticulous data expert with a strong command of multiple data processing tools, including Python and Excel. With my expertise in Data Analysis, Data Cleansing, and Data Processing, I am highly capable of tidying up your CSV/Excel files for you. I understand the importance of clean, well-formatted data for efficient downstream analysis. This means not only removing duplicates and treating missing values with logical defaults but also harmonizing the format of every entry in each column – whether it's dates, numbers or free text. I am flexible on the exact conventions and willing to work within your preferences to assure that your data is consistent, well-documented and ready to be smoothly integrated into your pipeline. In addition to my technical skills, which include proficiency in Pandas library for efficient data manipulation,I'm known for my strong work ethic, attention-to-detail

@SoneJ

Hello, I understand you need a thorough data cleanup for your CSV/Excel files, focusing on duplicates, missing values, and consistent formatting. My approach would involve a systematic review of your data using Python/pandas for efficiency and accuracy. I will ensure that all duplicates are removed, missing values are addressed appropriately, and all columns are formatted uniformly to facilitate seamless downstream analysis. To better tailor the solution, could you clarify how many files you'll be providing and the approximate size of the datasets? Additionally, do you have any specific conventions in mind for formatting, or should I propose a standard approach? Studio SJ operates on paid fixed-price, milestone, or hourly engagements. Would that model work for you? Sone Joubert Studio SJ