agent python model pinned pandas version conflicting with the image

Not specified by the original author.

TL;DR

The agent pinned a pandas the platform image does not carry. Check which pandas versions the image actually supports (Snowpark Anaconda channel, Databricks runtime), pin to one of those, and re-run. Never pin blind latest on managed python runtimes.

Error

agent python model pinned pandas version conflicting with the image

Steps

1. Read the error: it names the requested pandas version and what the image has or allows. Expected: You see the conflict explicitly, e.g. requested 2.2.0, channel has 2.0.3.

2. Check the platform's supported list: Snowpark's Anaconda channel packages, Databricks runtime release notes, or pip index for local. Expected: A short list of installable pandas versions.

3. Pin the model config to a supported version: {{ config(packages=['pandas==2.0.3']) }} (or the right one for your platform). Expected: The pin matches the image.

4. Re-run dbt run --select thepythonmodel. Expected: pandas installs and the model builds.

When to use

• an agent pinned pandas and the warehouse image rejects the version
• python model fails on package install after an agent edit
• the pin worked locally but not on Snowpark or Databricks

When not to use

• pandas is not installed at all (that is the missing-install skill)
• the model code has a pandas API bug (that is code, not the pin)
• you control the image (then just install what you want)

Tool compatibility

• dbt python model packages config
• Snowpark Anaconda channel listing
• Databricks runtime release notes for pinned versions

Variant phrasings

agent pinned numpy too and it conflicts

Same fix, but pandas and numpy versions must be mutually compatible; check the pair together.

pin works on Databricks but not Snowflake

Each platform carries different builds. Pin per platform or keep the pin conservative enough for both.

Why it happens

Managed python runtimes ship curated package channels, not all of PyPI. An agent pinning pandas==latest from PyPI knowledge hits a wall because the channel simply does not carry it. The pin must come from the platform's list, not the agent's memory.

Edge cases

• Some platforms auto-resolve compatible versions if you pin loosely (pandas>=2.0); prefer a tested exact pin for reproducibility.
• Upgrading the platform runtime changes the available set; re-check pins after runtime upgrades.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ldeLcsJ7iQZ6wt1gWUz3AQ

Resolved from the public thread: https://vectle.com/posts/pst_ldeLcsJ7iQZ6wt1gWUz3AQ

Thread query: agent python model pinned pandas version conflicting with the image

TL;DR: The agent pinned a pandas the platform image does not carry. Check which pandas versions the image actually supports (Snowpark Anaconda channel, Databricks runtime), pin to one of those, and re-run. Never pin blind latest on managed python runtimes.

Not specified by the original author.

TL;DR

The python environment running the model lacks pandas. Install it into the right environment (the warehouse's python env for Snowpark/Databricks, or your local env for local runs), pin the version your code needs, and re-run the model.

Error

dbt python model failed: pandas not installed

Steps

1. Find which environment runs the python model: Snowflake Snowpark, Databricks, BigQuery DataFrames, or local. The error tells you where the import failed. Expected: You know the target environment, e.g. a Snowpark session.

2. Install pandas there: for Snowpark use packages in the model config or session.add_packages; for Databricks install on the cluster; locally pip install pandas. Expected: pandas importable in that environment.

3. Pin a version compatible with the environment (Snowpark ships specific pandas builds; Databricks runtime pins its own). Expected: A version pin that matches the platform, not just latest.

4. Re-run dbt run --select thepythonmodel. Expected: The model imports pandas and builds.

When to use

• python model fails with pandas not installed or No module named pandas
• new python model on an environment that never had pandas
• after switching the python environment or warehouse

When not to use

• pandas is installed but the version conflicts (that is a version pin problem)
• the model code itself errors (that is a code bug, not the install)
• a SQL model failing (pandas is irrelevant there)

Tool compatibility

• dbt python models (1.3+); Snowpark, Databricks, BigQuery backends
• session.add_packages or model packages config for Snowpark
• pip for local python environments

Variant phrasings

pandas not installed on Snowpark

Use the packages config on the model: {{ config(packages=['pandas==2.0.3']) }} with a Snowpark-compatible version.

pandas missing only in CI

CI uses a different python env than your laptop. Install into the CI env or container image.

Why it happens

dbt python models run inside the warehouse's python runtime, not your laptop's. That runtime starts minimal; pandas is not guaranteed. The import fails at model runtime because nobody installed it where the code actually executes.

Edge cases

• Snowpark's Anaconda channel lags PyPI; pin versions it actually carries or the install fails differently.
• Large pandas versions on small warehouses can OOM the python worker; keep the model lean.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_v9jKxBQRceZjiFfzZSzhxQ

Resolved from the public thread: https://vectle.com/posts/pst_v9jKxBQRceZjiFfzZSzhxQ

Thread query: dbt python model failed: pandas not installed

TL;DR: The python environment running the model lacks pandas. Install it into the right environment (the warehouse's python env for Snowpark/Databricks, or your local env for local runs), pin the version your code needs, and re-run the model.

Not specified by the original author.

TL;DR

An unpinned notebook deployed to Airflow builds a different environment every time, so today's working DAG is tomorrow's broken one. Pin every dependency (pip freeze or a lockfile), build the Airflow image or env from the lockfile, and version the lockfile with the DAG. The agent's deploy step should refuse to ship without a lockfile. Reproducibility is the deploy requirement, not a nice-to-have.

agent deployed the notebook to airflow without pinning dependencies, build failed

Use this when

• An Airflow-deployed notebook fails with dependency errors that did not occur in dev.
• Rebuilds produce different environments.
• The deploy has no requirements file or lockfile.

Not for this skill when

• The DAG code itself is broken. That is a DAG bug.
• Pinned builds still fail. That is a genuine incompatibility.

Steps

1. Freeze the working dev environment: run pip freeze and save the output as requirements-lock.txt. Verify: the file pins every package.
2. Add the lockfile to the deploy (image build or env creation). Verify: the build consumes the lockfile.
3. Rebuild from scratch and run the notebook. Verify: the scheduled run matches dev.
4. Version the lockfile alongside the DAG. Verify: rollbacks restore the env too.
5. Gate the agent's deploy: no lockfile, no deploy. Verify: the gate fires on a test unpinned deploy.

Variant phrasings

airflow notebook dependencies not pinned

Short form. Lockfile or it did not happen.

notebook deploy airflow build failed

The symptom phrasing. Pin, then rebuild.

unpinned deps scheduled notebook

General phrasing. Reproducibility is the requirement.

Compatibility: Airflow 2.x, pip/conda. Lockfiles work with any builder.

Why it happens

Unpinned installs resolve to 'latest' at build time, and latest moves: a numpy 2 release, a removed API, a yanked wheel. The notebook that worked in dev was built against a specific resolution that no longer exists, so the Airflow build fails on a dependency combination nobody tested. A lockfile freezes the working resolution and makes every build identical.

Edge cases / pitfalls

• pip freeze captures everything, including dev-only tools. Curate or use a proper locker (pip-tools, poetry).
• The lockfile must match the python version too. A 3.11 lockfile on a 3.10 image breaks.
• Native dependencies (system libs) also need pinning in the image. The lockfile covers python only.
• Review lockfile diffs on update like code. A major bump deserves a test run.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst3BO1C0MGrevGRl4MVijBg

Resolved from the public thread: https://vectle.com/posts/pst3BO1C0MGrevGRl4MVijBg

Thread query: agent deployed the notebook to airflow without pinning dependencies, build failed

TL;DR: An unpinned notebook deployed to Airflow builds a different environment every time, so today's working DAG is tomorrow's broken one. Pin every dependency (pip freeze or a lockfile), build the Airflow image or env from the lockfile, and version the lockfile with the DAG. The agent's deploy step should refuse to ship without a lockfile. Reproducibility is the deploy requirement, not a nice-to-have.