## TL;DR
Fix this in CTGAN by acting on the exact message first: profile the mixed column for every distinct python type it holds, pick one target type, coerce stray values such as numbers stored as text or text stored in a numeric column, drop or recode the uncoercible rows with a count logged, and declare that type explicitly in the metadata before training. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one table, batch or record set first, then rerun the full scope and reconcile the counts.

```text
ValueError: Unable to identify column type - mixed type column training failed
```

## Use this when
- The operation in CTGAN fails with this exact message or symptom: ValueError: Unable to identify column type - mixed type column training failed.
- The same failure repeats on retry with unchanged inputs.
- Generation, validation or export is blocked until this item completes.

## Not for this skill when
- The message is an outage notice from the platform. Resolve that first, then return here if this error remains.
- The operation completes and you only disagree with its output quality. That is a quality review, not this failure.

## Steps
1. Reproduce the failure with the smallest scope that still shows it in CTGAN, and copy the full message plus the table, column or job involved into the run notes. Verify: the error is reproduced on demand with a named scope, not only seen in a log.
2. Apply the specific fix for this error: profile the mixed column for every distinct python type it holds, pick one target type, coerce stray values such as numbers stored as text or text stored in a numeric column, drop or recode the uncoercible rows with a count logged, and declare that type explicitly in the metadata before training. Verify: the smallest failing scope now completes, or the named column, table or job behaves as the fix predicts.
3. Check the neighbouring tables, columns or jobs that share the same config, schema or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in CTGAN on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, distributions or status against the source in CTGAN so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

## Why it happens
CTGAN infers a transformer per column from its values, and a column that mixes numbers, text and dates gives it no single type to choose, so training stops instead of guessing.

## Variant phrasings

### ValueError: Unable to identify column type - mixed type column training failed
The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single scope proof, then the full rerun.

### How to fix - CTGAN error "ValueError: Unable to identify column type" mixed type column training failed
The how-to phrasing for the same failure in CTGAN. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

### CTGAN troubleshooting for this failure
The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: CTGAN at current versions, plus the platform API or console the query names; no extra tooling beyond the source system access needed to verify counts.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_zVoLBNKailnHpSfFIZNejw