Welcome to the Dataramp documentation! Here you will find information about Dataramp, including some examples to get you started.
Dataramp is a Python library designed to streamline data science and data analysis workflows. It offers a collection of utility functions and tools tailored to assist data science teams in various aspects of their projects.
By providing a range of functionalities, Dataramp aims to enhance productivity and efficiency in data science projects, empowering teams to focus on deriving meaningful insights from their data.
Read the quick start guide here.
If you want to see some examples, you can look at the examples in the examples directory.
You can install Dataramp and learn more from PyPi.
# Create and register a model pipeline
preprocessor = Pipeline([
('scaler', StandardScaler()),
('imputer', SimpleImputer())
])
pipeline = Pipeline([
('preprocess', preprocessor),
('classifier', LogisticRegression())
])
model_save(pipeline, "classifier", method="joblib", metadata={"dataset": "2023_sales"})
register_model(
pipeline,
name="sales_classifier",
version="v1.0",
metadata={
"metrics": {"accuracy": 0.89},
"serialization_method": "joblib"
}
)
# Create versioned dataset
df = pd.read_csv("data.csv")
data_save(df, "processed_data", versioning=True, description="Initial cleaned version")- Data Science Projects : Initialize projects with a standardized structure and manage datasets and models effectively.
- Team Collaboration : Facilitate collaboration by providing clear project organization and versioning.
- Reproducibility : Ensure reproducibility by tracking dataset versions, model metadata, and dependencies.
- Automation : Integrate into CI/CD pipelines for automated testing, deployment, and dependency updates.