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?
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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?
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Stan MarshPosted Sep 17, 2025, 4:30 AM
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 HobbsPosted Sep 16, 2025, 3:13 PM
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 ChhayaPosted Sep 16, 2025, 7:12 AM
For cleaning messy Excel/CSV files in a student project:
Start with OpenRefine for ease; back up originals first!