CSV Cleaning Studio
Remove duplicates, fix missing values, standardise dates and export a clean CSV. Your file never leaves your device.
Data and ProductivityData and Productivity
Data and Productivity starts from a truth anyone who works with spreadsheets learns the hard way: most CSV files are slightly broken. Delimiters vary, encodings drift, dates arrive in four formats, duplicates hide, and blanks mean three different things. The CSV Cleaning Studio is built for that reality: it detects the delimiter and encoding, profiles every column's type, and gives you controlled operations — trimming, deduplication on the columns you choose, missing-value strategies (drop, fill, or mean/median/mode for numbers), renaming, reordering, case changes, date standardisation and row filters — each with a before-and-after preview and a summary of exactly what changed. When the data is clean, the Chart Builder takes over: pick columns, choose an aggregation, and build a bar, line, pie, scatter or histogram chart with honest handling of missing values, then export it as PNG or SVG. Both tools run entirely in your browser, so client data and exports never leave your machine.
Tools in this category
Chart Builder
Build charts from CSV data with aggregation, sorting and accessible colour palettes. Export as PNG or SVG.
Data and ProductivityWhich tool should I use?
| Tool | Use it when you need to… |
|---|---|
| CSV Cleaning Studio | Clean a CSV: remove duplicates, handle missing values, rename/reorder columns, standardise dates, then export safely. |
| Chart Builder | Chart a cleaned CSV with aggregation and sorting, then export the chart as PNG or SVG. |
Working notes
Clean in small, reversible steps and read the summary after each one. Apply trimming and deduplication first, check the rows-removed count against your expectation, then handle missing values, then standardise formats. If a count surprises you, undo and inspect the source column before continuing — a surprising number is information about your data, not an obstacle. Save the cleaned file under a new name and keep the original untouched; the pair of files, plus the change summary, is your audit trail if anyone later asks why a figure moved.
Related articles
How to Clean and Transform CSV Files
A step-by-step CSV cleaning workflow — delimiter and encoding first, then headers, whitespace, case, dates, missing values, duplicates and a formula-safe export — demonstrated on a deliberately messy 12-row file run through the CSV Cleaning Studio's own pipeline code, with its real change summary quoted.
How to Turn CSV Data Into Useful Charts
The step most chart tools skip: aggregating rows before you chart them. Using the cleaned 10-row orders file, this guide builds the exact grouping tables — totals by category, by month, by city — shows the arithmetic, names the missing month the grouping produces, and matches each table to the chart type its shape calls for.
How to Choose the Right Chart for Your Data
Choose a chart by the question, not by habit: comparison wants bars, trend wants a line, parts of a whole want few slices and honest percentages. One real dataset — order totals by city, every number computed from the same cleaned file — is presented three ways to show concretely where a pie chart misleads and a bar chart does not.
Frequently asked questions
What happens to my original CSV file?
It stays on your device and it is never modified. The Cleaning Studio parses a copy in your browser, shows before-and-after previews, and your download is a new file. An undo stack lets you walk back any operation.
How do I know the cleaner did not damage my data?
The summary panel lists rows removed and cells changed after every operation, and the preview shows the first 200 rows before and after. If a result surprises you, undo and adjust the settings instead of guessing.
How do I chart the file after cleaning it?
The Chart Builder accepts the cleaned file directly — download it from the Cleaning Studio, or use the handoff button — and lets you pick columns, aggregation and chart type, with warnings when values are missing or non-numeric.