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I will clean organize and analyse your excel or csv data using python
Data Analyst, Python, EDA Specialist
Hi, I'm Muhammad Umer — a Data Analyst from Pakistan.
I help businesses clean, analyse, and
visualize their data to make better decisions.
What I do:
- Data Cleaning (Python, Pandas, Excel)
- Explo...
Sobre este Serviço
Struggling with messy, incomplete, or unorganized data? I'll clean, structure,
and prepare your dataset so it's ready for analysis, reporting, or modeling
no manual Excel work needed, everything is processed with Python for accuracy
and consistency.
What I'll do:
- Remove duplicate rows and irrelevant columns
- Handle missing/blank values (fill or remove, based on your needs)
- Fix inconsistent formatting (dates, text case, spacing, number formats)
- Standardize column names and structure
- Merge multiple files into one clean dataset (if needed)
- Basic outlier detection
Tools: Python, Pandas, Excel/CSV
Delivery: A clean, ready-to-use file + a short summary explaining what was fixed
Certified in Data Analysis & Business Intelligence (DigiSkills)
Experience analyzing real-world datasets with 50,000+ rows see my portfolio below
Send me your file and let's get it sorted fast, accurate, and hassle-free!
Meu portfólio
Perguntas frequentes
What file formats do you support?
I work with Excel (xlsx, xls), CSV, and any other tabular data formats. If you are unsure, just send me a message!
Can you remove duplicates and fix errors?
Yes! I can remove duplicate records, fix formatting issues, handle missing values, and clean inconsistent data using Python Pandas.
Do you use Python for data cleaning?
Yes! I use Python, Pandas, and NumPy for advanced data cleaning and preprocessing. I have cleaned 100,000+ real records in my projects.
Will my data remain confidential?
Absolutely. Client data is kept private and used only for the requested project.
Can you handle large datasets?
Yes! Basic plan covers 1,000 rows, Standard covers 10,000 rows, and Premium handles even larger datasets. Contact me before ordering if you have a very large dataset!
