Python Library
Python library for Akkio
API Keys
As noted in the code samples below, you must get your API keys and copy them into your API code. Those can be found under the team settings page at the bottom of the Akkio app.
Installation
Example Usage
Datasets
create_dataset(dataset_name)
Create a new empty dataset.
input | description |
dataset_name | The name of your newly created dataset. |
add_rows_to_dataset(dataset_id, rows)
Add rows to a dataset.
input | description |
dataset_id | A dataset id |
rows | An array of rows to be added to the dataset in the following form: [{ "field 1": "data", "field 2": "data" }, { ... }, ... ] |
get_datasets()
Get all datasets in your organization.
get_dataset(dataset_id)
Get a dataset.
input | description |
dataset_id | A dataset id |
parse_dataset(dataset_id)
Recalculate the field types for a dataset.
input | description |
dataset_id | A dataset id |
delete_dataset(dataset_id)
Delete a dataset.
input | description |
dataset_id | A dataset id |
Models
get_models()
Get all models in your organization.
delete_model(model_id)
Delete a model in your organization.
input | description |
model_id | A model id |
create_model(dataset_id, predict_fields, ignore_fields, params)
Create a model (requires a dataset).
input | description |
dataset_id | A dataset id |
predict_fields | An array of field names to predict (case sensitive) |
ignore_fields | An array of field names to ignore (case sensitive) (optional) |
params | A dict with default value of:
|
Sometimes creating models can take a while, especially if this is the first time creating a model on this dataset. create_model is idempotent and can be called multiple times with the same parameters.
make_prediction(model_id, data)
Make a prediction using your model and new data.
input | description |
model_id | A model id |
data | An array of rows to be predicted in the following form: [{ "field 1": "data", "field 2": "data" }, { ... }, ... ] |
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