Step 1 of 4

Upload a dataset

Drop a CSV, Excel, JSON, or Parquet file. PrePro Auto reads it, detects the encoding and delimiter automatically, and creates a job you can run the whole pipeline on.

Drop your file here

or click to browse · csv, xlsx, json, parquet · max 200 MB

Domain

Step 2 of 4

Profile the data

Profiling infers each column's true type, missing rate, and quality, and detects ordered categories. This must run before any cleaning stage.

Step 3 of 6

View dataset

A live view of your data at the current version. It updates after every change anywhere — cleaning stages, transforms, undo/redo. Toggle between the original (raw) data and the current state.

Original (raw)

Current

Step 4 of 6

Clean the data

Each stage proposes decisions. Approve, override, or skip each one, then execute the stage to apply them and write a new snapshot. Stages run in order.

Approve — accept PrePro Auto's recommended action as-is.

Override — reject the recommendation and pick a different action from the dropdown (the alternatives PrePro Auto considered). Use this when you know better, e.g. keep outliers PrePro Auto wanted to cap.

Skip — leave this column unchanged for this stage.

Step 5 of 6

Transform

Full manual control over the dataset: create, drop, rename, merge, cast, filter, map, and apply math to columns and rows. Every transform is a new version — undo any time.

Preset operations

Expression (advanced)

AI assistant

Visualize & metrics

Dashboard (before/after)

Data drift

Operation

Columns — click to select one or more (applies the operation to each selected column where relevant)

Tip: select multiple columns to run the same operation on all of them at once.

Pandas expression — the dataframe is df; pd and np are available.

Assign result to column (leave blank if expression returns a full table)

Click to try: df["revenue"] - df["cost"] np.log1p(df["Votes"]) (df["Aggregate rating"] > 4).astype(int)

Expressions run in a locked-down sandbox: no imports, file, network, or OS access, and no .str/.apply methods. Enter only the right-hand-side value (not df[...] = ...). To pull numbers out of text like "2BHK" → 2, use the Extract number from text preset instead. Safe for local use.

Chat with your data

Tell it what to change in plain English. It confirms what it understood, shows a 2-row preview, and waits for your approval before touching anything.

Need data-prep advice instead? (what should I do for my goal)

Your goal

Target column (optional)

Who will use the result? (optional)

Nothing is applied until you click Approve. Approving runs the change on your full dataset as one undoable step. You can also copy the code into the Expression tab. Requires an LLM key in .env.

Chart builder

Metric counter

Raw vs current

Ask (AI)

Chart type

Condition over df — count rows where this is true. e.g. df["bath"] > df["bhk"] + 2

Column to compare (raw vs current)

Chart

Ask a question about your data in plain English. The AI picks the metric or chart; the numbers are computed locally.

Power-BI-style overview comparing the raw dataset to the current cleaned version. KPIs at the top, then per-column changes.

Per-column comparison

Upload two datasets to detect distribution drift (e.g. training data vs new production data). Uses PSI for both numeric and categorical columns, plus the KS test for numerics. No model required.

Reference dataset (baseline / training)

New dataset (recent / production)

Step 6 of 6

Results & export

See exactly what preprocessing changed — the quality score, the column types before vs after, and a data sample — then download your deliverables.

Before & after

Comparison of the raw upload against the cleaned result.

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Download

Take your results away.

Clean data

The processed dataset as CSV or Parquet.

Audit report

A PDF listing every transformation and who decided it.

Pipeline code

A standalone Python script reproducing the cleaning.