openlayer.Project.add_dataframe

Project.add_dataframe(*args, **kwargs)

Adds a dataset (Pandas dataframe) to a project’s staging area.

Parameters:

dataset_dfpd.DataFrame

Dataframe with your dataset.

dataset_config: Dict[str, any]

Dictionary containing the dataset configuration. This is not needed if dataset_config_file_path is provided.

What’s in the dataset config?

The dataset configuration depends on the project’s tasks.TaskType. Refer to the How to write dataset configs guides for details.

dataset_config_file_pathstr

Path to the dataset configuration YAML file. This is not needed if dataset_config is provided.

What’s in the dataset config file?

The dataset configuration YAML depends on the project’s tasks.TaskType. Refer to the How to write dataset configs guides for details.

forcebool

If add_dataset is called when there is already a dataset of the same type in the staging area, when force=True, the existing staged dataset will be overwritten by the new one. When force=False, the user will be prompted to confirm the overwrite first.

Notes

Your dataset is in csv file? You can use the add_dataset method instead.

Examples

Related guide: How to upload datasets and models for development.

First, instantiate the client:

>>> import openlayer
>>>
>>> client = openlayer.OpenlayerClient('YOUR_API_KEY_HERE')

Create a project if you don’t have one:

>>> from openlayer.tasks import TaskType
>>>
>>> project = client.create_project(
...     name="Churn Prediction",
...     task_type=TaskType.TabularClassification,
...     description="My first project!",
... )

If you already have a project created on the platform:

>>> project = client.load_project(name="Your project name")

Let’s say you have a tabular classification project and your dataset looks like the following:

>>> df
            CreditScore  Geography    Balance  Churned
0               618       France       321.92     1
1               714      Germany      102001.22   0
2               604       Spain       12333.15    0

Prepare the dataset config:

>>> dataset_config = {
...     'classNames': ['Retained', 'Churned'],
...     'labelColumnName': 'Churned',
...     'label': 'training',  # or 'validation'
...     'featureNames': ['CreditScore', 'Geography', 'Balance'],
...     'categoricalFeatureNames': ['Geography'],
... }

What’s in the dataset config?

The dataset configuration depends on the project’s tasks.TaskType. Refer to the How to write dataset configs guides for details.

You can now add this dataset to your project with:

>>> project.add_dataset(
...     dataset_df=df,
...     dataset_config=dataset_config,
... )

After adding the dataset to the project, it is staged, waiting to be committed and pushed to the platform.

You can check what’s on your staging area with status. If you want to push the dataset right away with a commit message, you can use the commit and push methods:

>>> project.commit("Initial dataset commit.")
>>> project.push()