Before training any models or creating any projects, you need to upload your data into a Dataset.

Creating A Dataset

There are several ways to create a Dataset. Dataset.create_from_file can take either a path to a local file or any stream-able file object.

>>> import datarobot as dr
>>> dataset = dr.Dataset.create_from_file(file_path='data_dir/my_data.csv')
>>> with open('data_dir/my_data.csv', 'rb') as f:
...     other_dataset = dr.Dataset.create_from_file(filelike=f)

Dataset.create_from_in_memory_data can take either a pandas.Dataframe or a list of dictionaries representing rows of data. Note that the dictionaries representing the rows of data must contain the same keys.

>>> import pandas as pd
>>> data_frame = pd.read_csv('data_dir/my_data.csv')

# do things to my data_frame
>>> pandas_dataset = dr.Dataset.create_from_in_memory_data(data_frame=data_frame)

>>> in_memory_data = [{'key1': 'value', 'key2': 'other_value', ...},
...                   {'key1': 'new_value', 'key2': 'other_new_value', ...}, ...]
>>> in_memory_dataset = dr.Dataset.create_from_in_memory_data(records=other_data)

Dataset.create_from_url takes csv data from a URL. If you have not set ENABLE_CREATE_SNAPSHOT_DATASOURCE, you must set do_snapshot=False.

>>> url_dataset = dr.Dataset.create_from_url('',
...                                          do_snapshot=False)

Using Datasets

Once a Dataset is created, you can create Projects from it and then begin training on the projects. (You can also combine project creation and uploading Dataset in a single step in Project.create. However, this means the data is only accessible to the project which created it.)

>>> project = dataset.create_project(project_name='New Project')
>>> project.set_target('some target')
Project(New Project)

Getting Information From A Dataset

The dataset object contains some basic information:

>>> dataset.categories
>>> dataset.created_at
datetime.datetime(2020, 2, 7, 16, 51, 10, 311000, tzinfo=tzutc())

There are several methods to get details from a Dataset.

# Details
>>> details = dataset.get_details()
>>> details.last_modification_date
datetime.datetime(2020, 2, 7, 16, 51, 10, 311000, tzinfo=tzutc())
>>> details.feature_count_by_type
[FeatureTypeCount(count=1, feature_type=u'Text'),
 FeatureTypeCount(count=1, feature_type=u'Boolean'),
 FeatureTypeCount(count=16, feature_type=u'Numeric'),
 FeatureTypeCount(count=3, feature_type=u'Categorical')]
>>> details.to_dataset().id == details.dataset_id

# Projects
>>> dr.Project.create_from_dataset(, project_name='Project One')
Project(Project One)
>>> dr.Project.create_from_dataset(, project_name='Project Two')
Project(Project Two)
>>> dataset.get_projects()
[ProjectLocation(url=u'', id=u'5e3c94aff86f2d10692497b5'),
 ProjectLocation(url=u'', id=u'5e3c94eb9525d010a9918ec1')]
>>> first_id = dataset.get_projects()[0].id
>>> dr.Project.get(first_id).project_name
'Project One'

# Features
>>> all_features = dataset.get_all_features()
>>> feature = next(dataset.iterate_all_features(offset=2, limit=1))
>>> == all_features[2].name
>>> print(, feature.feature_type, feature.dataset_id)
(u'Partition', u'Numeric', u'5e31cdac39782d0f65842518')
>>> feature.get_histogram().plot
[{'count': 3522, 'target': None, 'label': u'0.0'},
 {'count': 3521, 'target': None, 'label': u'1.0'}, ... ]

# The raw data
>>> with open('myfile.csv', 'wb') as f:
...     dataset.get_file(filelike=f)

Retrieving Datasets

You can retrieve either specific datasets, the list of all datasets or an iterator that can get all or some of the datasets.

>>> dataset_id = '5e387c501a438646ed7bf0f2'
>>> dataset = dr.Dataset.get(dataset_id)
>>> == dataset_id
# a blocking call that returns all datasets
>>> dr.Dataset.list()
[Dataset(name=u'Untitled Dataset', id=u'5e3c51e0f86f2d1087249728'),
 Dataset(name=u'my_data.csv', id=u'5e3c2028162e6a5fe9a0d678'), ...]

# avoid listing Datasets that failed to properly upload
>>> dr.Dataset.list(filter_failed=True)
[Dataset(name=u'my_data.csv', id=u'5e3c2028162e6a5fe9a0d678'),
 Dataset(name=u'my_other_data.csv', id=u'3efc2428g62eaa5f39a6dg7a'), ...]

# an iterator that lazily retrieves from the server page-by-page
>>> from itertools import islice
>>> iterator = dr.Dataset.iterate(offset=2)
>>> for element in islice(iterator, 3):
...    print(element)
Dataset(name='some_data.csv', id='5e8df2f21a438656e7a23d12')
Dataset(name='other_data.csv', id='5e8df2e31a438656e7a23d0b')
Dataset(name='Untitled Dataset', id='5e6127681a438666cc73c2b0')

Managing Datasets

You can modify, delete and un_delete datasets. Note that you need the dataset’s ID in order to un_delete it and if you do not keep track of this it will be gone. If your deleted dataset had been used to create a project, that project can still access it, but you will not be able to create new projects using that dataset.

>>> dataset.modify(name='A Better Name')
'A Better Name'

>>> new_project = dr.Project.create_from_dataset(
>>> stored_id =
>>> dr.Dataset.delete(

# new_project is still ok
>>> dr.Project.create_from_dataset(stored_id)
Traceback (most recent call last):
datarobot.errors.ClientError: 410 client error: {u'message': u'Requested Dataset 5e31cdac39782d0f65842518 was previously deleted.'}

>>> dr.Dataset.un_delete(stored_id)
>>> dr.Project.create_from_dataset(stored_id, project_name='Successful')

Managing Dataset Featurelists

You can create, modify, and delete custom featurelists on a given dataset. Some featurelists are automatically created by DataRobot and can not be modified or deleted. There is no option to un_delete a deleted featurelist.

>>> dataset.get_featurelists()
[DatasetFeaturelist(Raw Features),
 DatasetFeaturelist(Informative Features)]

>>> dataset_features = [ for feature in dataset.get_all_features()]
>>> custom_featurelist = dataset.create_featurelist('Custom Features', dataset_features[:5])
>>> custom_featurelist
DatasetFeaturelist(Custom Features)

>>> dataset.get_featurelists()
[DatasetFeaturelist(Raw Features),
 DatasetFeaturelist(Informative Features),
 DatasetFeaturelist(Custom Features)]

>>> custom_featurelist.update('New Name')
'New Name'

>>> custom_featurelist.delete()
>>> dataset.get_featurelists()
[DatasetFeaturelist(Raw Features),
 DatasetFeaturelist(Informative Features)]