Best Tools for Cleaning Messy Data

Question

I’m working on a student project with messy Excel and CSV files. What are the best tools or libraries to clean and prepare data for analysis?

Stan Marsh · Answer

Easiest GUI: OpenRefine to fix messy text, split/merge columns, and remove duplicates with “clustering.” Great for quick cleanup before analysis. Simple code path: pandas for reading CSV/Excel and cleaning; add pyjanitor for handy shortcuts like clean_names() and remove_empty(). Keep data honest: Use Pandera or Great Expectations to set rules (types, ranges, categories) so bad files fail fast. If staying in Excel: Use Power Query to remove duplicates, trim spaces, standardize dates, and merge files in a repeatable way. Quick start (Python): Install: pip install pandas pyjanitor pandera Flow: read file → clean_names() → fix types → drop duplicates → fill/flag nulls → validate → save clean CSV.

Sam Hobbs · Answer

I think it depends on how it is messy. Are the problems something that software knows what the problems are without being told? If not then you need to define the problems and solutions. That would be the hard part.

Jayraj Chhaya · Answer

For cleaning messy Excel/CSV files in a student project: Pandas (Python) : Load, dedupe, handle missing data via code. OpenRefine : Free desktop tool for interactive cleaning—no code needed. Google Sheets/Excel : Use built-in Remove Duplicates, Power Query for quick fixes. Start with OpenRefine for ease; back up originals first!

Best Tools for Cleaning Messy Data

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