simple-pipelines 0.1.0

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SimplePipeline

A lightweight Python library for building and executing data pipelines with logging and error handling.

📖 Features

✅ Define data pipelines with processing steps, conditions, and outputs
✅ Support multiple ingests (data sources)
✅ Graceful failure handling with logging
✅ Customizable logging with plug-and-play loggers (FileLogger)
✅ Filter logs by level (INFO, SUCCESS, ERROR)
✅ Visualize the pipeline with an interactive graph

📥 Installation

Prerequisites

Ensure you have Python 3.9+ installed. You can check your Python version with:

python --version

Installing SimplePipeline

To install SimplePipeline, run the following command:

pip install simple-pipelines

📝 Usage

Creating a Pipeline

To create a pipeline, import the SimplePipeline class from the simple_pipelines module:

from simple_pipelines import SimplePipeline

Create a new pipeline instance:

pipeline = SimplePipeline("My Pipeline")

Adding Ingests

Ingests are functions that return data. You can add ingests to your pipeline using the create_ingest method:

def ingest_users():
    return pd.DataFrame({
        "ID": [1, 2, 3, 4, 5],
        "Age": [25, 32, 40, 23, 36],
        "Score": [80, 90, 85, 78, 88]
    })

pipeline.create_ingest(ingest_users, "Users")

The create_ingest method takes two arguments: the ingest function and a name for the ingest. The ingest function should return a pandas.DataFrame or a dictionary.

Adding Steps

Steps are functions that process data. You can add steps to your pipeline using the pipe method:

def process_users(input, ingests):
    df = ingests["Users"].copy()
    df["B"] = df["Age"] * 2
    return df

pipeline.pipe(process_users, "Process Users")

The pipe method takes two arguments: the step function and a name for the step. The step function should take two arguments: input, which is the output of the previous step, and ingests, which is a dictionary containing all ingests. The step function should return the processed data.

Adding Conditions

Conditions are functions that evaluate whether a step should be executed. You can add conditions to your pipeline using the condition method:

def is_vip(input, ingests):
    return input["Purchase_Amount"].sum() > 150

def branch_vip(input, ingests):
    input["Classification"] = "VIP"
    return input

def branch_regular(input, ingests):
    input["Classification"] = "Regular"
    return input

pipeline.condition(
    conditions={is_vip: branch_vip},
    default_branch=branch_regular,
    name="Check VIP Status"
)

The condition method takes three arguments: a dictionary of conditions and their corresponding branches, a default branch, and a name for the condition. The conditions should be functions that take two arguments: input, which is the output of the previous step, and ingests, which is a dictionary containing all ingests. The branches should be functions that take two arguments: input, which is the output of the previous step, and ingests, which is a dictionary containing all ingests. The default branch should be a function that takes two arguments: input, which is the output of the previous step, and ingests, which is a dictionary containing all ingests.

Adding Outputs

Outputs are functions that do not return data. You can add outputs to your pipeline using the output method:

def final_output(input):
    print("Final Processed Data:")
    print(input)

pipeline.output(final_output, "Final Output")

The output method takes two arguments: the output function and a name for the output. The output function should take one argument: input, which is the output of the previous step.

Executing the Pipeline

To execute the pipeline, call the execute method:

pipeline.execute()

The execute method returns the output of the last step, or None if the pipeline fails.

Visualizing the Pipeline

To visualize the pipeline, call the visualize method:

pipeline.visualize()

This will generate an interactive left-to-right visualization of the pipeline using Plotly without Graphviz.

📝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your changes.
3. Make your changes and commit them.
4. Push your changes to your forked repository.
5. Create a pull request to the main repository.

📝 License

This project is licensed under the MIT License.