To help reuse logic in Hamilton, you can use decorators such as mapping business concepts to function names and ensuring naming uniformity across the code base (source: [Hamilton - Function Naming Best Practices](https://hamilton.dagworks.io/en/latest/concepts/best-practices/function-naming/)). If you'd like to learn more about the benefits of using Hamilton, you can visit the following resources: - [Hamilton - Main](https://hamilton.dagworks.io/en/latest/) - [Hamilton - Function Naming Best Practices](https://hamilton.dagworks.io/en/latest/concepts/best-practices/function-naming/)

@Stefan Krawczyk asked "what are some Hamilton decorators that I can use to help me reuse logic? (testing out a bot against our docs)" Let me see what I can find... 🤔

To reuse logic in Hamilton, you can make use of the following decorators: 1. check_output 2. config.when 3. does 4. extract_columns 5. extract_fields 6. inject 7. load_from For more information, you can refer to the Hamilton decorators documentation [here](https://hamilton.dagworks.io/en/latest/reference/decorators/). If there are specific use cases for custom decorators, it is recommended to reach out on Slack or via a GitHub issue to discuss and ensure alignment with the API.

To use the

check_output

decorator in Hamilton, you can add simple data quality checks to your code by following the example provided in the documentation. Here's how you would do it:

python
import pandas as pd
import numpy as np
from hamilton.function_modifiers import check_output

@check_output(
    data_type=np.int64,
    range=(0,100),
)
def some_int_data_between_0_and_100() -> pd.Series:
    # Your code here
    pass

This decorator enables you to specify the data type and range for the output, allowing you to enforce data quality constraints. For more details, you can refer to the official documentation on the

check_output

decorator in Hamilton [here](https://hamilton.dagworks.io/en/latest/reference/decorators/check_output/).

{ "text": "The

@step

decorator is not mentioned in the provided documents, so there is no information available about it." }

@ThreadScribe what can you tell me about the

@pipe

decorator?

I'm sorry. Our search came up empty. Please modify your question and try again.

@Stefan Krawczyk asked "what can you tell me about the

@pipe

decorator?" Let me see what I can find... 🤔

To understand the

@pipe

decorator, we can refer to the Hamilton documentation. The

@pipe

decorator is used to represent a chained set of transformations. It is specifically designed to solve the "node redefinition" problem and is meant to represent a pipeline of chaining/redefinitions. This can be used to display transformations as nodes in the DAG, store or visualize the result, pull in functions from an external repository, build the DAG more procedurally, and use the same function multiple times with different parameters. It is a part of the function modifiers in Hamilton, which are highly expressive and useful for writing expressive dataflows. For more details, the documentation can be found at [Hamilton @pipe Documentation](https://hamilton.dagworks.io/en/latest/reference/decorators/pipe/).

i'll keep you posted

@ThreadScribe what can you tell me about the

@pipe

decorator?

The

@pipe

decorator in Hamilton is used to represent a chained set of transformations, providing a solution to the "node redefinition" problem and representing a pipeline of chaining/redefinitions. It is intended to be used for the following reasons: 1. To display transformations as nodes in the DAG, with the possibility of storing or visualizing the result. 2. To pull in functions from an external repository and build the DAG more procedurally. 3. To use the same function multiple times, but with different parameters. The

@pipe

decorator allows for the chaining of nodes, and it works perfectly with various types of data such as dataframes and series. For more detailed information, the documentation for the

@pipe

decorator can be found at pipe - Hamilton and Function modifiers (Advanced).