Dagster Pandas Utilities

0.29.0 · active · verified Sat Apr 11

dagster-pandas is a library within the Dagster ecosystem that provides utilities for working with Pandas DataFrames. It enhances Dagster's capabilities by offering DataFrame-level validation, summary statistics generation, and reliable serialization/deserialization for Pandas objects. Currently at version 0.29.0, its release cadence is tied closely to the main Dagster core releases.

Warnings

breaking Dagster core and library versions are tightly coupled. While libraries like `dagster-pandas` follow their own semantic versioning, major changes in Dagster core can necessitate upgrades or adjustments in library usage. Always check the Dagster core changelog for breaking changes relevant to your setup.
Fix: Consult the Dagster documentation and release notes for both `dagster` and `dagster-pandas` when upgrading. Test your pipelines thoroughly after any version bump.
gotcha Features such as `create_dagster_pandas_dataframe_type` and `PandasColumn` are often marked as 'beta' or 'preview' in the Dagster API. This means they might introduce breaking changes in minor versions or have behavior changes in patch releases.
Fix: Review the API lifecycle stages documentation for any components you use. Be prepared for potential adjustments if relying on beta/preview features, especially during upgrades.
gotcha Pandas operations, especially on large DataFrames, can be memory-intensive and lead to Out Of Memory (OOM) errors. This is a common pitfall when processing big datasets within Dagster pipelines without proper memory management or architectural considerations.
Fix: For large datasets, consider techniques like chunked processing, optimizing DataFrame data types, or using more memory-efficient alternatives if the problem persists. Focus on I/O and query optimization before Python code optimization.
gotcha Dagster's layered execution model can sometimes make Python stack traces less developer-friendly, obscuring the direct cause of errors within your Pandas transformation logic.
Fix: Utilize Dagster's structured logging (`context.log`) within your assets to emit clear messages and intermediate values. Isolate and test Pandas logic outside the Dagster environment during development to debug complex issues more easily.
gotcha Python version compatibility is crucial. While `dagster-pandas` specifies `Python <3.15, >=3.10`, Dagster core regularly drops support for Python versions that reach End Of Life (EOL). Running on an unsupported Python version can lead to unexpected issues.
Fix: Regularly check Dagster's Python version support in its documentation. Ensure your environment uses a Python version compatible with both your Dagster core and `dagster-pandas` installations.

Install

pip install dagster-pandas Install dagster-pandas

Imports

asset
```
from dagster import asset
```
Definitions
```
from dagster import Definitions
```

load_assets_from_modules

from dagster import load_assets_from_modules

create_dagster_pandas_dataframe_type
```
from dagster_pandas import create_dagster_pandas_dataframe_type
```
Used for creating custom Dagster types with Pandas DataFrame schema validation.
PandasColumn
```
from dagster_pandas import PandasColumn
```
Used in conjunction with `create_dagster_pandas_dataframe_type` for column-level constraints.

Quickstart

This quickstart demonstrates defining two Dagster assets using Pandas. The `raw_data_csv` asset simulates loading data into a DataFrame, and `processed_data` transforms it by filtering based on age. To run, save the code as a Python file, execute `dagster dev -f <your_file.py>`, and then use the Dagster UI to materialize the assets.

import os
import pandas as pd
from dagster import asset, Definitions, load_assets_from_modules

# --- defs/data/sample_data.csv ---
# name,age,city
# Alice,25,New York
# Bob,35,San Francisco
# Charlie,45,Chicago
# Diana,28,Boston

# --- defs/assets.py ---
@asset
def raw_data_csv() -> pd.DataFrame:
    # In a real scenario, this would read from a persistent store, e.g., S3 or a database
    # For quickstart, we simulate by creating a DataFrame
    data = {
        'name': ['Alice', 'Bob', 'Charlie', 'Diana'],
        'age': [25, 35, 45, 28],
        'city': ['New York', 'San Francisco', 'Chicago', 'Boston']
    }
    return pd.DataFrame(data)

@asset
def processed_data(raw_data_csv: pd.DataFrame) -> pd.DataFrame:
    return raw_data_csv[raw_data_csv['age'] > 30].copy()

# --- definitions.py ---
# Assuming assets.py is in a 'defs' directory or in the same file for quickstart

all_assets = load_assets_from_modules([__name__]) # Load assets from this file

defs = Definitions(assets=all_assets)

# To run this, save as a Python file (e.g., my_project.py) and run:
# dagster dev -f my_project.py
# Then open http://localhost:3000 and materialize 'processed_data'

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