Clean data made easy

Your analysis is only as good as the quality of the data. Coco makes this incredibly important part easy, and seemless

Edit cells directly

See an issue? Click into the cell to modify all versions of that value, such an incorrectly spelled category. See updates in real time, even with billions of rows.

Clean data with natural language

Tell Coco which changes you'd like, and watch them get done for you 10x faster.

Profile without writing a single line of code

View distributions and value counts with just a click. Both update as changes are made, and are fully interactive.

Distributions

Explore how your values are distributed, with adjustable grouping and statistical measures included.

Distribution chart showing the frequency of values as a teal area graph.

Value counts

See how often each value appears. Search, rename, merge, or convert values to null.

Value counts showing risk categories and the number of rows for each value, sorted by frequency.

Clean data using distributions

Sometimes it's easier to remove a whole set of data just by looking at the distribution. Click and drag to Merge, Keep Only, or Exclude rows from your selection

Write custom SQL

In addition to changes in the UI, Coco supports a full SQL dialect based on DuckDB – CocoSQL. Create a new column, or write a full query write in the same interface.

Quickly merge and join data

Easily merge one or more datasets, with column differences being handled for you. Quickly move columns from one dataset to another.

Slice-and-dice the whole dataset

Coco provides a truely interactive hybrid experience, allowing you to easily delete, move or modify columns. Filter interactively, copy, and more.

Keep track of every change, automatically

Prepare your data without changing the underlying source. Coco tracks your work automatically, so you can revisit old work, or try something new.

Coco Change History showing two sales sources joined by a union, followed by Create Column, Trim, and Nullify changes, with the selected column expression and resulting data below.

Try it on your data

Clean and prepare your data with every change tracked along the way.

Questions? Answers.