## 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

```text
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 the_python_model.
   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