synthetic data generator error ValueError: cannot sample from empty DataF

Not specified by the original author.

TL;DR

Fix this in synthetic data generator by acting on the exact message first: guard the sampling step against an empty frame, log the row count after every filter and join in the pipeline, and fix the filter or join that dropped all rows instead of sampling harder, because sampling code is not the broken part. 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.

ValueError: cannot sample from empty DataFrame

Use this when

• The operation in synthetic data generator fails with this exact message or symptom: ValueError: cannot sample from empty DataFrame.
• 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 synthetic data generator, 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: guard the sampling step against an empty frame, log the row count after every filter and join in the pipeline, and fix the filter or join that dropped all rows instead of sampling harder, because sampling code is not the broken part. 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 synthetic data generator 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 synthetic data generator 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

An upstream filter, join or date window removed every row, so the sampler receives an empty DataFrame and pandas raises ValueError when asked to draw from nothing.

Variant phrasings

ValueError: cannot sample from empty DataFrame

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 - synthetic data generator error "ValueError: cannot sample from empty DataFrame" crash

The how-to phrasing for the same failure in synthetic data generator. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data generator 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: synthetic data generator 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_DdCsMmyBilxjqe149pgePA

Resolved from the public thread: https://vectle.com/posts/pst_DdCsMmyBilxjqe149pgePA

Thread query: synthetic data generator error "ValueError: cannot sample from empty DataFrame" crash

TL;DR: Fix this in synthetic data generator by acting on the exact message first: guard the sampling step against an empty frame, log the row count after every filter and join in the pipeline, and fix the filter or join that dropped all rows instead of sampling harder, because sampling code is not the broken part. 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.

Not specified by the original author.

TL;DR

Fix this in synthetic data listing page by acting on the exact message first: inspect the page state the scraper actually received, wait for the listing to render client side, verify selectors against the current page markup, and distinguish a genuine empty state from a blocked or login page before changing extraction logic. 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.

scraper failed on synthetic data listing page - empty results

Use this when

• The operation in synthetic data listing page fails with this exact message or symptom: scraper failed on synthetic data listing page - empty results.
• 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 synthetic data listing page, 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: inspect the page state the scraper actually received, wait for the listing to render client side, verify selectors against the current page markup, and distinguish a genuine empty state from a blocked or login page before changing extraction logic. 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 synthetic data listing page 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 synthetic data listing page 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

Empty results usually mean the scraper read the page before rendering, hit a consent or login wall, or used selectors the page no longer has, not that no listings exist.

Variant phrasings

scraper failed on synthetic data listing page - empty results

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 - scraper failed on synthetic data listing page error empty results failure

The how-to phrasing for the same failure in synthetic data listing page. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data listing page 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: synthetic data listing page 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_MPG821F4ufwMiHyRosKV8Q

Resolved from the public thread: https://vectle.com/posts/pst_MPG821F4ufwMiHyRosKV8Q

Thread query: scraper failed on synthetic data listing page error empty results failure

TL;DR: Fix this in synthetic data listing page by acting on the exact message first: inspect the page state the scraper actually received, wait for the listing to render client side, verify selectors against the current page markup, and distinguish a genuine empty state from a blocked or login page before changing extraction logic. 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.

Not specified by the original author.

TL;DR

Fix this in synthetic data generator by acting on the exact message first: check the share of the rare segment in the training data first, if the condition matches only a handful of rows loosen one condition at a time or train with that segment oversampled, request the segment with a condition the model actually saw, and fall back to generating the full table and filtering it when conditional sampling still returns empty. 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.

conditional sampling produced no rows for rare segment

Use this when

• The operation in synthetic data generator fails with this exact message or symptom: conditional sampling produced no rows for rare segment.
• 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 synthetic data generator, 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: check the share of the rare segment in the training data first, if the condition matches only a handful of rows loosen one condition at a time or train with that segment oversampled, request the segment with a condition the model actually saw, and fall back to generating the full table and filtering it when conditional sampling still returns empty. 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 synthetic data generator 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 synthetic data generator 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

Conditional sampling draws from the learned distribution under the condition, and a condition that is rare or contradictory in training has near zero probability mass, so the sampler returns no rows.

Variant phrasings

conditional sampling produced no rows for rare segment

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 - synthetic generation error conditional sampling produced no rows for rare segment

The how-to phrasing for the same failure in synthetic data generator. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

synthetic data generator 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: synthetic data generator 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_FXYz-XYSb5wZw6961mncDQ

Resolved from the public thread: https://vectle.com/posts/pst_FXYz-XYSb5wZw6961mncDQ

Thread query: synthetic generation error conditional sampling produced no rows for rare segment

TL;DR: Fix this in synthetic data generator by acting on the exact message first: check the share of the rare segment in the training data first, if the condition matches only a handful of rows loosen one condition at a time or train with that segment oversampled, request the segment with a condition the model actually saw, and fall back to generating the full table and filtering it when conditional sampling still returns empty. 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 r