> For the complete documentation index, see [llms.txt](https://docs.akkio.com/akkio-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akkio.com/akkio-docs/endpoints-and-schemas/additional-libraries/python.md).
# 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](https://app.akkio.com/team-settings) at the bottom of the Akkio app.
## Installation
```bash
pip install akkio
```
## Example Usage
```python
import akkio
akkio.api_key = 'YOUR-API-KEY-HERE'
# get your API key at https://app.akkio.com/team-settings
# list models in your organization
models = akkio.get_models()['models']
for model in models:
print(model)
# list datasets in your organization
datasets = akkio.get_datasets()['datasets']
for dataset in datasets:
print(dataset)
# create a new empty dataset
new_dataset = akkio.create_dataset('python api test')
print(new_dataset)
# add rows to the dataset
import random
rows = []
for i in range(1000):
rows.append({
'x': random.random()
})
rows[-1]['y'] = rows[-1]['x'] > 0.5
akkio.add_rows_to_dataset(new_dataset['dataset_id'], rows)
# create a model
new_model = akkio.create_model(new_dataset['dataset_id'], ['y'], [], {'duration': 1})
print(new_model)
# make a prediction using the model
prediction = akkio.make_prediction(new_model['model_id'], [{'x': 0.1}, {'x':0.7}], explain=True)
print(prediction)
```
## 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:
|
###
### 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:
{ "duration": 10,
"extra\_attention": False,
"force": False }
duration is the duration in seconds to be used for model training.
extra\_attention can be enabled to help with predicting rare cases
force forces a new model to be created
|
{% hint style="info" %}
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.
{% endhint %}
###
### 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:
|
---
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