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Python models

Note that only specific data platforms support dbt-py models.

We encourage you to:


dbt Python (dbt-py) models can help you solve use cases that can't be solved with SQL. You can perform analyses using tools available in the open-source Python ecosystem, including state-of-the-art packages for data science and statistics. Before, you would have needed separate infrastructure and orchestration to run Python transformations in production. Python transformations defined in dbt are models in your project with all the same capabilities around testing, documentation, and lineage.

import ...

def model(dbt, session):

my_sql_model_df = dbt.ref("my_sql_model")

final_df = ... # stuff you can't write in SQL!

return final_df
version: 2

- name: my_python_model

# Document within the same codebase
description: My transformation written in Python

# Configure in ways that feel intuitive and familiar
materialized: table
tags: ['python']

# Test the results of my Python transformation
- name: id
# Standard validation for 'grain' of Python results
- unique
- not_null
# Write your own validation logic (in SQL) for Python results
- custom_generic_test
SQL + Python, together at lastSQL + Python, together at last

The prerequisites for dbt Python models include using an adapter for a data platform that supports a fully featured Python runtime. In a dbt Python model, all Python code is executed remotely on the platform. None of it is run by dbt locally. We believe in clearly separating model definition from model execution. In this and many other ways, you'll find that dbt's approach to Python models mirrors its longstanding approach to modeling data in SQL.

We've written this guide assuming that you have some familiarity with dbt. If you've never before written a dbt model, we encourage you to start by first reading dbt Models. Throughout, we'll be drawing connections between Python models and SQL models, as well as making clear their differences.

What is a Python model?

A dbt Python model is a function that reads in dbt sources or other models, applies a series of transformations, and returns a transformed dataset. DataFrame operations define the starting points, the end state, and each step along the way.

This is similar to the role of CTEs in dbt SQL models. We use CTEs to pull in upstream datasets, define (and name) a series of meaningful transformations, and end with a final select statement. You can run the compiled version of a dbt SQL model to see the data included in the resulting view or table. When you dbt run, dbt wraps that query in create view, create table, or more complex DDL to save its results in the database.

Instead of a final select statement, each Python model returns a final DataFrame. Each DataFrame operation is "lazily evaluated." In development, you can preview its data, using methods like .show() or .head(). When you run a Python model, the full result of the final DataFrame will be saved as a table in your data warehouse.

dbt Python models have access to almost all of the same configuration options as SQL models. You can test and document them, add tags and meta properties, and grant access to their results to other users. You can select them by their name, file path, configurations, whether they are upstream or downstream of another model, or if they have been modified compared to a previous project state.

Defining a Python model

Each Python model lives in a .py file in your models/ folder. It defines a function named model(), which takes two parameters:

  • dbt: A class compiled by dbt Core, unique to each model, enables you to run your Python code in the context of your dbt project and DAG.
  • session: A class representing your data platform’s connection to the Python backend. The session is needed to read in tables as DataFrames, and to write DataFrames back to tables. In PySpark, by convention, the SparkSession is named spark, and available globally. For consistency across platforms, we always pass it into the model function as an explicit argument called session.

The model() function must return a single DataFrame. On Snowpark (Snowflake), this can be a Snowpark or pandas DataFrame. Via PySpark (Databricks + BigQuery), this can be a Spark, pandas, or pandas-on-Spark DataFrame. For more about choosing between pandas and native DataFrames, see DataFrame API + syntax.

When you dbt run --select python_model, dbt will prepare and pass in both arguments (dbt and session). All you have to do is define the function. This is how every single Python model should look:

def model(dbt, session):


return final_df

Referencing other models

Python models participate fully in dbt's directed acyclic graph (DAG) of transformations. Use the dbt.ref() method within a Python model to read data from other models (SQL or Python). If you want to read directly from a raw source table, use dbt.source(). These methods return DataFrames pointing to the upstream source, model, seed, or snapshot.

def model(dbt, session):

# DataFrame representing an upstream model
upstream_model = dbt.ref("upstream_model_name")

# DataFrame representing an upstream source
upstream_source = dbt.source("upstream_source_name", "table_name")


Of course, you can ref() your Python model in downstream SQL models, too:

with upstream_python_model as (

select * from {{ ref('my_python_model') }}



Referencing ephemeral models is currently not supported (see feature request)

Configuring Python models

Just like SQL models, there are three ways to configure Python models:

  1. In dbt_project.yml, where you can configure many models at once
  2. In a dedicated .yml file, within the models/ directory
  3. Within the model's .py file, using the dbt.config() method

Calling the dbt.config() method will set configurations for your model within your .py file, similar to the {{ config() }} macro in .sql model files:

def model(dbt, session):

# setting configuration

There's a limit to how complex you can get with the dbt.config() method. It accepts only literal values (strings, booleans, and numeric types) and dynamic configuration. Passing another function or a more complex data structure is not possible. The reason is that dbt statically analyzes the arguments to config() while parsing your model without executing your Python code. If you need to set a more complex configuration, we recommend you define it using the config property in a YAML file.

Accessing project context

dbt Python models don't use Jinja to render compiled code. Python models have limited access to global project contexts compared to SQL models. That context is made available from the dbt class, passed in as an argument to the model() function.

Out of the box, the dbt class supports:

  • Returning DataFrames referencing the locations of other resources: dbt.ref() + dbt.source()
  • Accessing the database location of the current model: dbt.this() (also: dbt.this.database, .schema, .identifier)
  • Determining if the current model's run is incremental: dbt.is_incremental

It is possible to extend this context by "getting" them with dbt.config.get() after they are configured in the model's config. Starting from dbt v1.8, the dbt.config.get() method supports dynamic access to configurations within Python models, enhancing flexibility in model logic. This includes inputs such as var, env_var, and target. If you want to use those values for the conditional logic in your model, we require setting them through a dedicated YAML file config:

version: 2

- name: my_python_model
materialized: table
target_name: "{{ }}"
specific_var: "{{ var('SPECIFIC_VAR') }}"
specific_env_var: "{{ env_var('SPECIFIC_ENV_VAR') }}"

Then, within the model's Python code, use the dbt.config.get() function to access values of configurations that have been set:

def model(dbt, session):
target_name = dbt.config.get("target_name")
specific_var = dbt.config.get("specific_var")
specific_env_var = dbt.config.get("specific_env_var")

orders_df = dbt.ref("fct_orders")

# limit data in dev
if target_name == "dev":
orders_df = orders_df.limit(500)


Python models support these materializations:

  • table (default)
  • incremental

Incremental Python models support all the same incremental strategies as their SQL counterparts. The specific strategies supported depend on your adapter. As an example, incremental models are supported on BigQuery with Dataproc for the merge incremental strategy; the insert_overwrite strategy is not yet supported.

Python models can't be materialized as view or ephemeral. Python isn't supported for non-model resource types (like tests and snapshots).

For incremental models, like SQL models, you need to filter incoming tables to only new rows of data:

import snowflake.snowpark.functions as F

def model(dbt, session):
dbt.config(materialized = "incremental")
df = dbt.ref("upstream_table")

if dbt.is_incremental:

# only new rows compared to max in current table
max_from_this = f"select max(updated_at) from {dbt.this}"
df = df.filter(df.updated_at >= session.sql(max_from_this).collect()[0][0])

# or only rows from the past 3 days
df = df.filter(df.updated_at >= F.dateadd("day", F.lit(-3), F.current_timestamp()))


return df

Python-specific functionality

Defining functions

In addition to defining a model function, the Python model can import other functions or define its own. Here's an example on Snowpark, defining a custom add_one function:

def add_one(x):
return x + 1

def model(dbt, session):
temps_df = dbt.ref("temperatures")

# warm things up just a little
df = temps_df.withColumn("degree_plus_one", add_one(temps_df["degree"]))
return df

Currently, Python functions defined in one dbt model can't be imported and reused in other models. Refer to Code reuse for the potential patterns being considered.

Using PyPI packages

You can also define functions that depend on third-party packages so long as those packages are installed and available to the Python runtime on your data platform. See notes on "Installing Packages" for specific data platforms.

In this example, we use the holidays package to determine if a given date is a holiday in France. The code below uses the pandas API for simplicity and consistency across platforms. The exact syntax, and the need to refactor for multi-node processing, still vary.

import holidays

def is_holiday(date_col):
# Chez Jaffle
french_holidays = holidays.France()
is_holiday = (date_col in french_holidays)
return is_holiday

def model(dbt, session):
materialized = "table",
packages = ["holidays"]

orders_df = dbt.ref("stg_orders")

df = orders_df.to_pandas()

# apply our function
# (columns need to be in uppercase on Snowpark)
df["IS_HOLIDAY"] = df["ORDER_DATE"].apply(is_holiday)
df["ORDER_DATE"].dt.tz_localize('UTC') # convert from Number/Long to tz-aware Datetime

# return final dataset (Pandas DataFrame)
return df

Configuring packages

We encourage you to configure required packages and versions so dbt can track them in project metadata. This configuration is required for the implementation on some platforms. If you need specific versions of packages, specify them.

def model(dbt, session):
packages = ["numpy==1.23.1", "scikit-learn"]
version: 2

- name: my_python_model
- "numpy==1.23.1"
- scikit-learn

User-defined functions (UDFs)

You can use the @udf decorator or udf function to define an "anonymous" function and call it within your model function's DataFrame transformation. This is a typical pattern for applying more complex functions as DataFrame operations, especially if those functions require inputs from third-party packages.

import snowflake.snowpark.types as T
import snowflake.snowpark.functions as F
import numpy

def register_udf_add_random():
add_random = F.udf(
# use 'lambda' syntax, for simple functional behavior
lambda x: x + numpy.random.normal(),
return add_random

def model(dbt, session):

materialized = "table",
packages = ["numpy"]

temps_df = dbt.ref("temperatures")

add_random = register_udf_add_random()

# warm things up, who knows by how much
df = temps_df.withColumn("degree_plus_random", add_random("degree"))
return df

Note: Due to a Snowpark limitation, it is not currently possible to register complex named UDFs within stored procedures and, therefore, dbt Python models. We are looking to add native support for Python UDFs as a project/DAG resource type in a future release. For the time being, if you want to create a "vectorized" Python UDF via the Batch API, we recommend either:

Code reuse

Currently, Python functions defined in one dbt model can't be imported and reused in other models. This is something dbt Labs would like to support, so there are two patterns we're considering:

  • Creating and registering "named" UDFs This process is different across data platforms and has some performance limitations. For example, Snowpark supports vectorized UDFs for pandas-like functions that you can execute in parallel.
  • Private Python packages In addition to importing reusable functions from public PyPI packages, many data platforms support uploading custom Python assets and registering them as packages. The upload process looks different across platforms, but your code’s actual import looks the same.
❓ dbt questions
  • Should dbt have a role in abstracting over UDFs? Should dbt support a new type of DAG node, function? Would the primary use case be code reuse across Python models or defining Python-language functions that can be called from SQL models?
  • How can dbt help users when uploading or initializing private Python assets? Is this a new form of dbt deps?
  • How can dbt support users who want to test custom functions? If defined as UDFs: "unit testing" in the database? If "pure" functions in packages: encourage adoption of pytest?

💬 Discussion: "Python models: package, artifact/object storage, and UDF management in dbt"

DataFrame API and syntax

Over the past decade, most people writing data transformations in Python have adopted DataFrame as their common abstraction. dbt follows this convention by returning ref() and source() as DataFrames, and it expects all Python models to return a DataFrame.

A DataFrame is a two-dimensional data structure (rows and columns). It supports convenient methods for transforming that data and creating new columns from calculations performed on existing columns. It also offers convenient ways for previewing data while developing locally or in a notebook.

That's about where the agreement ends. There are numerous frameworks with their own syntaxes and APIs for DataFrames. The pandas library offered one of the original DataFrame APIs, and its syntax is the most common to learn for new data professionals. Most newer DataFrame APIs are compatible with pandas-style syntax, though few can offer perfect interoperability. This is true for Snowpark and PySpark, which have their own DataFrame APIs.

When developing a Python model, you will find yourself asking these questions:

Why pandas? It's the most common API for DataFrames. It makes it easy to explore sampled data and develop transformations locally. You can “promote” your code as-is into dbt models and run it in production for small datasets.

Why not pandas? Performance. pandas runs "single-node" transformations, which cannot benefit from the parallelism and distributed computing offered by modern data warehouses. This quickly becomes a problem as you operate on larger datasets. Some data platforms support optimizations for code written using pandas DataFrame API, preventing the need for major refactors. For example, pandas on PySpark offers support for 95% of pandas functionality, using the same API while still leveraging parallel processing.

❓ dbt questions
  • When developing a new dbt Python model, should we recommend pandas-style syntax for rapid iteration and then refactor?
  • Which open source libraries provide compelling abstractions across different data engines and vendor-specific APIs?
  • Should dbt attempt to play a longer-term role in standardizing across them?

💬 Discussion: "Python models: the pandas problem (and a possible solution)"


Python models have capabilities that SQL models do not. They also have some drawbacks compared to SQL models:

  • Time and cost. Python models are slower to run than SQL models, and the cloud resources that run them can be more expensive. Running Python requires more general-purpose compute. That compute might sometimes live on a separate service or architecture from your SQL models. However: We believe that deploying Python models via dbt—with unified lineage, testing, and documentation—is, from a human standpoint, dramatically faster and cheaper. By comparison, spinning up separate infrastructure to orchestrate Python transformations in production and different tooling to integrate with dbt is much more time-consuming and expensive.
  • Syntax differences are even more pronounced. Over the years, dbt has done a lot, via dispatch patterns and packages such as dbt_utils, to abstract over differences in SQL dialects across popular data warehouses. Python offers a much wider field of play. If there are five ways to do something in SQL, there are 500 ways to write it in Python, all with varying performance and adherence to standards. Those options can be overwhelming. As the maintainers of dbt, we will be learning from state-of-the-art projects tackling this problem and sharing guidance as we develop it.
  • These capabilities are very new. As data warehouses develop new features, we expect them to offer cheaper, faster, and more intuitive mechanisms for deploying Python transformations. We reserve the right to change the underlying implementation for executing Python models in future releases. Our commitment to you is around the code in your model .py files, following the documented capabilities and guidance we're providing here.
  • Lack of print() support. The data platform runs and compiles your Python model without dbt's oversight. This means it doesn't display the output of commands such as Python's built-in print() function in dbt's logs.

As a general rule, if there's a transformation you could write equally well in SQL or Python, we believe that well-written SQL is preferable: it's more accessible to a greater number of colleagues, and it's easier to write code that's performant at scale. If there's a transformation you can't write in SQL, or where ten lines of elegant and well-annotated Python could save you 1000 lines of hard-to-read Jinja-SQL, Python is the way to go.

Specific data platforms

In their initial launch, Python models are supported on three of the most popular data platforms: Snowflake, Databricks, and BigQuery/GCP (via Dataproc). Both Databricks and GCP's Dataproc use PySpark as the processing framework. Snowflake uses its own framework, Snowpark, which has many similarities to PySpark.

Additional setup: You will need to acknowledge and accept Snowflake Third Party Terms to use Anaconda packages.

Installing packages: Snowpark supports several popular packages via Anaconda. Refer to the complete list for more details. Packages are installed when your model is run. Different models can have different package dependencies. If you use third-party packages, Snowflake recommends using a dedicated virtual warehouse for best performance rather than one with many concurrent users.

Python version: To specify a different python version, use the following configuration:

def model(dbt, session):
materialized = "table",

About "sprocs": dbt submits Python models to run as stored procedures, which some people call sprocs for short. By default, dbt will create a named sproc containing your model's compiled Python code, and then call it to execute. Snowpark has an Open Preview feature for temporary or anonymous stored procedures (docs), which are faster and leave a cleaner query history. You can switch this feature on for your models by configuring use_anonymous_sproc: True. We plan to switch this on for all dbt + Snowpark Python models starting with the release of dbt Core version 1.4.

# I asked Snowflake Support to enable this Private Preview feature,
# and now my dbt-py models run even faster!
use_anonymous_sproc: True

Docs: "Developer Guide: Snowpark Python"