Prophet failed error "Dataframe has less than 2 non-NaN rows" in anomaly pipeline

Not specified by the original author.

TL;DR

Fix this in Prophet by acting on the exact message first: clean the frame before fitting, drop the rows with missing values and confirm at least two dated observations remain, and if cleaning empties the frame fix the upstream filter that removed everything. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Dataframe has less than 2 non-NaN rows

Use this when

• The operation in Prophet fails with this exact message: Dataframe has less than 2 non-NaN rows.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or 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. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Prophet, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: clean the frame before fitting, drop the rows with missing values and confirm at least two dated observations remain, and if cleaning empties the frame fix the upstream filter that removed everything. Verify: the cleaned frame keeps enough rows and the fit completes.
3. Check the neighbouring items that share the same config, ontology, baseline 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 Prophet 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, decisions or alerts against the source in Prophet 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

Missing value handling upstream can leave almost every row blank; Prophet needs at least two real observations to fit anything.

Variant phrasings

Dataframe has less than 2 non-NaN rows

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: Prophet failed error "Dataframe has less than 2 non-NaN rows" in anomaly pipeline

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

Prophet 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: Prophet at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_65i6QJ1VckVoAuqPXjwGgA

Resolved from the public thread: https://vectle.com/posts/pst_65i6QJ1VckVoAuqPXjwGgA

Thread query: Prophet failed error "Dataframe has less than 2 non-NaN rows" in anomaly pipeline

TL;DR: Fix this in Prophet by acting on the exact message first: clean the frame before fitting, drop the rows with missing values and confirm at least two dated observations remain, and if cleaning empties the frame fix the upstream filter that removed everything. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in the anomaly detector by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

input DataFrame must have exactly two columns: ds and y

Use this when

• The operation in the anomaly detector fails with this exact message: input DataFrame must have exactly two columns: ds and y.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or 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. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the anomaly detector, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Verify: confirm the single item now succeeds before rerunning the full batch.
3. Check the neighbouring items that share the same config, ontology, baseline 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. Compare the result counts, decisions or alerts against the source in the anomaly detector so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation in the anomaly detector 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.
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

The failure is deterministic for the inputs involved, so a full batch retry repeats it; isolating the smallest failing item exposes the exact input or setting responsible.

Variant phrasings

input DataFrame must have exactly two columns: ds and y

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: forecasting model failed error "input DataFrame must have exactly two

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

the anomaly detector 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: the anomaly detector at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_VS4mycziXkG4J1ANr2cbKg

Resolved from the public thread: https://vectle.com/posts/pst_VS4mycziXkG4J1ANr2cbKg

Thread query: forecasting model failed error "input DataFrame must have exactly two columns: ds and y"

TL;DR: Fix this in the anomaly detector by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

Fix this in Prophet by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Prophet changepoint detection failure error overfitting noise as anomaly

Use this when

• The operation in Prophet fails with this exact message: Prophet changepoint detection failure error overfitting noise as anomaly.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or 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. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Prophet, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Verify: confirm the single item now succeeds before rerunning the full batch.
3. Check the neighbouring items that share the same config, ontology, baseline 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 Prophet 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, decisions or alerts against the source in Prophet 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

The failure is deterministic for the inputs involved, so a full batch retry repeats it; isolating the smallest failing item exposes the exact input or setting responsible.

Variant phrasings

Prophet changepoint detection failure error overfitting noise as anoma

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: Prophet changepoint detection failure error overfitting noise as anoma

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

Prophet 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: Prophet at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_0MqRroVzwicKuFKIDQJknw

Resolved from the public thread: https://vectle.com/posts/pst_0MqRroVzwicKuFKIDQJknw

Thread query: Prophet changepoint detection failure error overfitting noise as anomaly

TL;DR: Fix this in Prophet by acting on the exact message first: reproduce the failure on the smallest single item first, note the exact inputs that trigger it, and change only the input or setting the error names. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.