Dataset Class

  • Reference

Represents a resource for exploring, transforming, and managing data in Azure Machine Learning.

A Dataset is a reference to data in a Datastore or behind public web urls.

For methods deprecated in this class, please check AbstractDataset class for the improved APIs.

The following Datasets types are supported:

  • TabularDataset represents data in a tabular format created by parsing the provided file or list of files.

  • FileDataset references single or multiple files in datastores or from public URLs.

To get started with datasets, see the article Add & register datasets, or see the notebooks https://aka.ms/tabulardataset-samplenotebook and https://aka.ms/filedataset-samplenotebook.

Initialize the Dataset object.

To obtain a Dataset that has already been registered with the workspace, use the get method.

Inheritance
builtins.object
Dataset

Constructor

Dataset(definition, workspace=None, name=None, id=None)

Parameters

Name Description
definition
Required
<xref:azureml.data.DatasetDefinition>

The Dataset definition.
workspace
Required
Workspace

The workspace in which the Dataset exists.
name
Required
str

The name of the Dataset.
id
Required
str

The unique identifier of the Dataset.

Remarks

The Dataset class exposes two convenience class attributes (File and Tabular) you can use for creating a Dataset without working with the corresponding factory methods. For example, to create a dataset using these attributes:

  • Dataset.Tabular.from_delimited_files()

  • Dataset.File.from_files()

You can also create a new TabularDataset or FileDataset by directly calling the corresponding factory methods of the class defined in TabularDatasetFactory and FileDatasetFactory.

The following example shows how to create a TabularDataset pointing to a single path in a datastore.


   from azureml.core import Dataset
   dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, 'train-dataset/tabular/iris.csv')])

   # preview the first 3 rows of the dataset
   dataset.take(3).to_pandas_dataframe()

Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/train-with-datasets/train-with-datasets.ipynb

Variables

Name Description
azureml.core.Dataset.File

A class attribute that provides access to the FileDatasetFactory methods for creating new FileDataset objects. Usage: Dataset.File.from_files().
azureml.core.Dataset.Tabular

A class attribute that provides access to the TabularDatasetFactory methods for creating new TabularDataset objects. Usage: Dataset.Tabular.from_delimited_files().

Methods

archive

Archive an active or deprecated dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
auto_read_files

Analyzes the file(s) at the specified path and returns a new Dataset.

Note

This method is deprecated and will no longer be supported.

Recommend to use the Dataset.Tabular.from_* methods to read files. For more information, see https://aka.ms/dataset-deprecation.
compare_profiles

Compare the current Dataset's profile with another dataset profile.

This shows the differences in summary statistics between two datasets. The parameter 'rhs_dataset' stands for "right-hand side", and is simply the second dataset. The first dataset (the current dataset object) is considered the "left-hand side".

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
create_snapshot

Create a snapshot of the registered Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
delete_snapshot

Delete snapshot of the Dataset by name.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
deprecate

Deprecate an active dataset in a workspace by another dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
diff

Diff the current Dataset with rhs_dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
from_binary_files

Create an unregistered, in-memory Dataset from binary files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.File.from_files instead. For more information, see https://aka.ms/dataset-deprecation.
from_delimited_files

Create an unregistered, in-memory Dataset from delimited files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_delimited_files instead. For more information, see https://aka.ms/dataset-deprecation.


   # Create a dataset from delimited files with header option as ALL_FILES_HAVE_SAME_HEADERS
   dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'data/crime-spring.csv'),
       header='ALL_FILES_HAVE_SAME_HEADERS')

   df = dataset.to_pandas_dataframe()
from_excel_files

Create an unregistered, in-memory Dataset from Excel files.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
from_json_files

Create an unregistered, in-memory Dataset from JSON files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_json_lines_files instead to read from JSON lines file. For more information, see https://aka.ms/dataset-deprecation.
from_pandas_dataframe

Create an unregistered, in-memory Dataset from a pandas dataframe.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.register_pandas_dataframe instead. For more information, see https://aka.ms/dataset-deprecation.
from_parquet_files

Create an unregistered, in-memory Dataset from parquet files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_parquet_files instead. For more information, see https://aka.ms/dataset-deprecation.
from_sql_query

Create an unregistered, in-memory Dataset from a SQL query.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_sql_query instead. For more information, see https://aka.ms/dataset-deprecation.
generate_profile

Generate new profile for the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
get

Get a Dataset that already exists in the workspace by specifying either its name or ID.

Note

This method is deprecated and will no longer be supported.

Recommend to use get_by_name and get_by_id instead. For more information, see https://aka.ms/dataset-deprecation.
get_all

Get all the registered datasets in the workspace.
get_all_snapshots

Get all snapshots of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
get_by_id

Get a Dataset which is saved to the workspace.
get_by_name

Get a registered Dataset from workspace by its registration name.
get_definition

Get a specific definition of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
get_definitions

Get all the definitions of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
get_profile

Get summary statistics on the Dataset computed earlier.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
get_snapshot

Get snapshot of the Dataset by name.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
head

Pull the specified number of records specified from this Dataset and returns them as a DataFrame.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
list

List all the Datasets in the workspace, including ones with is_visible property equal to False.

Note

This method is deprecated and will no longer be supported.

Recommend to use get_all instead. For more information, see https://aka.ms/dataset-deprecation.
reactivate

Reactivate an archived or deprecated dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
register

Register the Dataset in the workspace, making it available to other users of the workspace.

Note

This method is deprecated and will no longer be supported.

Recommend to use register instead. For more information, see https://aka.ms/dataset-deprecation.
sample

Generate a new sample from the source Dataset, using the sampling strategy and parameters provided.

Note

This method is deprecated and will no longer be supported.

Create a TabularDataset by calling the static methods on Dataset.Tabular and use the take_sample method there. For more information, see https://aka.ms/dataset-deprecation.
to_pandas_dataframe

Create a Pandas dataframe by executing the transformation pipeline defined by this Dataset definition.

Note

This method is deprecated and will no longer be supported.

Create a TabularDataset by calling the static methods on Dataset.Tabular and use the to_pandas_dataframe method there. For more information, see https://aka.ms/dataset-deprecation.
to_spark_dataframe

Create a Spark DataFrame that can execute the transformation pipeline defined by this Dataset definition.

Note

This method is deprecated and will no longer be supported.

Create a TabularDataset by calling the static methods on Dataset.Tabular and use the to_spark_dataframe method there. For more information, see https://aka.ms/dataset-deprecation.
update

Update the Dataset mutable attributes in the workspace and return the updated Dataset from the workspace.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.
update_definition

Update the Dataset definition.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

archive

Archive an active or deprecated dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

archive()

Returns

Type Description
None

None.

Remarks

After archival, any attempt to consume the Dataset will result in an error. If archived by accident, reactivate will activate it.

auto_read_files

Analyzes the file(s) at the specified path and returns a new Dataset.

Note

This method is deprecated and will no longer be supported.

Recommend to use the Dataset.Tabular.from_* methods to read files. For more information, see https://aka.ms/dataset-deprecation.

static auto_read_files(path, include_path=False, partition_format=None)

Parameters

Name Description
path
Required
DataReference or str

A data path in a registered datastore, a local path, or an HTTP URL(CSV/TSV).
include_path
Required
bool

Whether to include a column containing the path of the file from which the data was read. Useful when reading multiple files, and want to know which file a particular record originated from. Also useful if there is information in file path or name that you want in a column.
partition_format
Required
str

Specify the partition format in path and create string columns from format '{x}' and datetime column from format '{x:yyyy/MM/dd/HH/mm/ss}', where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extrat year, month, day, hour, minute and second for the datetime type. The format should start from the postition of first partition key until the end of file path. For example, given a file path '../Accounts/2019/01/01/data.csv' where data is partitioned by department name and time, we can define '/{Department}/{PartitionDate:yyyy/MM/dd}/data.csv' to create columns 'Department' of string type and 'PartitionDate' of datetime type.

Returns

Type Description
Dataset

Dataset object.

Remarks

Use this method when to have file formats and delimiters detected automatically.

After creating a Dataset, you should use get_profile to list detected column types and summary statistics for each column.

The returned Dataset is not registered with the workspace.

compare_profiles

Compare the current Dataset's profile with another dataset profile.

This shows the differences in summary statistics between two datasets. The parameter 'rhs_dataset' stands for "right-hand side", and is simply the second dataset. The first dataset (the current dataset object) is considered the "left-hand side".

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

compare_profiles(rhs_dataset, profile_arguments={}, include_columns=None, exclude_columns=None, histogram_compare_method=HistogramCompareMethod.WASSERSTEIN)

Parameters

Name Description
rhs_dataset
Required
Dataset

A second Dataset, also called a "right-hand side" Dataset for comparision.
profile_arguments
Required
dict

Arguments to retrive specific profile.
include_columns
Required
list[str]

List of column names to be included in comparison.
exclude_columns
Required
list[str]

List of column names to be excluded in comparison.
histogram_compare_method
Required
HistogramCompareMethod

Enum describing the comparison method, ex: Wasserstein or Energy

Returns

Type Description
<xref:azureml.dataprep.api.engineapi.typedefinitions.DataProfileDifference>

Difference between the two dataset profiles.

Remarks

This is for registered Datasets only. Raises an exception if the current Dataset's profile does not exist. For unregistered Datasets use profile.compare method.

create_snapshot

Create a snapshot of the registered Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

create_snapshot(snapshot_name, compute_target=None, create_data_snapshot=False, target_datastore=None)

Parameters

Name Description
snapshot_name
Required
str

The snapshot name. Snapshot names should be unique within a Dataset.
compute_target
Required
Union[ComputeTarget, str]

Optional compute target to perform the snapshot profile creation. If omitted, the local compute is used.
create_data_snapshot
Required
bool

If True, a materialized copy of the data will be created.
target_datastore
Required
Union[AbstractAzureStorageDatastore, str]

Target datastore to save snapshot. If omitted, the snapshot will be created in the default storage of the workspace.

Returns

Type Description
DatasetSnapshot

Dataset snapshot object.

Remarks

Snapshots capture point in time summary statistics of the underlying data and an optional copy of the data itself. To learn more about creating snapshots, go to https://aka.ms/azureml/howto/createsnapshots.

delete_snapshot

Delete snapshot of the Dataset by name.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

delete_snapshot(snapshot_name)

Parameters

Name Description
snapshot_name
Required
str

The snapshot name.

Returns

Type Description
None

None.

Remarks

Use this to free up storage consumed by data saved in snapshots that you no longer need.

deprecate

Deprecate an active dataset in a workspace by another dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

deprecate(deprecate_by_dataset_id)

Parameters

Name Description
deprecate_by_dataset_id
Required
str

The Dataset ID which is the intended replacement for this Dataset.

Returns

Type Description
None

None.

Remarks

Deprecated Datasets will log warnings when they are consumed. Deprecating a dataset deprecates all its definitions.

Deprecated Datasets can still be consumed. To completely block a Dataset from being consumed, archive it.

If deprecated by accident, reactivate will activate it.

diff

Diff the current Dataset with rhs_dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

diff(rhs_dataset, compute_target=None, columns=None)

Parameters

Name Description
rhs_dataset
Required
Dataset

Another Dataset also called right hand side Dataset for comparision
compute_target
Required
Union[ComputeTarget, str]

compute target to perform the diff. If omitted, the local compute is used.
columns
Required
list[str]

List of column names to be included in diff.

Returns

Type Description
DatasetActionRun

Dataset action run object.

from_binary_files

Create an unregistered, in-memory Dataset from binary files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.File.from_files instead. For more information, see https://aka.ms/dataset-deprecation.

static from_binary_files(path)

Parameters

Name Description
path
Required
DataReference or str

A data path in a registered datastore or a local path.

Returns

Type Description
Dataset

The Dataset object.

Remarks

Use this method to read files as streams of binary data. Returns one file stream object per file read. Use this method when you're reading images, videos, audio or other binary data.

get_profile and create_snapshot will not work as expected for a Dataset created by this method.

The returned Dataset is not registered with the workspace.

from_delimited_files

Create an unregistered, in-memory Dataset from delimited files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_delimited_files instead. For more information, see https://aka.ms/dataset-deprecation.


   # Create a dataset from delimited files with header option as ALL_FILES_HAVE_SAME_HEADERS
   dataset = Dataset.Tabular.from_delimited_files(path=(datastore, 'data/crime-spring.csv'),
       header='ALL_FILES_HAVE_SAME_HEADERS')

   df = dataset.to_pandas_dataframe()

static from_delimited_files(path, separator=',', header=PromoteHeadersBehavior.ALL_FILES_HAVE_SAME_HEADERS, encoding=FileEncoding.UTF8, quoting=False, infer_column_types=True, skip_rows=0, skip_mode=SkipLinesBehavior.NO_ROWS, comment=None, include_path=False, archive_options=None, partition_format=None)

Parameters

Name Description
path
Required
DataReference or str

A data path in a registered datastore, a local path, or an HTTP URL.
separator
Required
str

The separator used to split columns.
header
Required
PromoteHeadersBehavior

Controls how column headers are promoted when reading from files.
encoding
Required
FileEncoding

The encoding of the files being read.
quoting
Required
bool

Specify how to handle new line characters within quotes. The default (False) is to interpret new line characters as starting new rows, irrespective of whether the new line characters are within quotes or not. If set to True, new line characters inside quotes will not result in new rows, and file reading speed will slow down.
infer_column_types
Required
bool

Indicates whether column data types are inferred.
skip_rows
Required
int

How many rows to skip in the file(s) being read.
skip_mode
Required
SkipLinesBehavior

Controls how rows are skipped when reading from files.
comment
Required
str

Character used to indicate comment lines in the files being read. Lines beginning with this string will be skipped.
include_path
Required
bool

Whether to include a column containing the path of the file from which the data was read. This is useful when you are reading multiple files, and want to know which file a particular record originated from, or to keep useful information in file path.
archive_options
Required
<xref:azureml.dataprep.ArchiveOptions>

Options for archive file, including archive type and entry glob pattern. We only support ZIP as archive type at the moment. For example, specifying


   archive_options = ArchiveOptions(archive_type = ArchiveType.ZIP, entry_glob = '*10-20.csv')

reads all files with name ending with "10-20.csv" in ZIP.
partition_format
Required
str

Specify the partition format in path and create string columns from format '{x}' and datetime column from format '{x:yyyy/MM/dd/HH/mm/ss}', where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extrat year, month, day, hour, minute and second for the datetime type. The format should start from the postition of first partition key until the end of file path. For example, given a file path '../Accounts/2019/01/01/data.csv' where data is partitioned by department name and time, we can define '/{Department}/{PartitionDate:yyyy/MM/dd}/data.csv' to create columns 'Department' of string type and 'PartitionDate' of datetime type.

Returns

Type Description
Dataset

Dataset object.

Remarks

Use this method to read delimited text files when you want to control the options used.

After creating a Dataset, you should use get_profile to list detected column types and summary statistics for each column.

The returned Dataset is not registered with the workspace.

from_excel_files

Create an unregistered, in-memory Dataset from Excel files.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

static from_excel_files(path, sheet_name=None, use_column_headers=False, skip_rows=0, include_path=False, infer_column_types=True, partition_format=None)

Parameters

Name Description
path
Required
DataReference or str

A data path in a registered datastore or a local path.
sheet_name
Required
str

The name of the Excel sheet to load. By default we read the first sheet from each Excel file.
use_column_headers
Required
bool

Controls whether to use the first row as column headers.
skip_rows
Required
int

How many rows to skip in the file(s) being read.
include_path
Required
bool

Whether to include a column containing the path of the file from which the data was read. This is useful when you are reading multiple files, and want to know which file a particular record originated from, or to keep useful information in file path.
infer_column_types
Required
bool

If true, column data types will be inferred.
partition_format
Required
str

Specify the partition format in path and create string columns from format '{x}' and datetime column from format '{x:yyyy/MM/dd/HH/mm/ss}', where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extrat year, month, day, hour, minute and second for the datetime type. The format should start from the postition of first partition key until the end of file path. For example, given a file path '../Accounts/2019/01/01/data.xlsx' where data is partitioned by department name and time, we can define '/{Department}/{PartitionDate:yyyy/MM/dd}/data.xlsx' to create columns 'Department' of string type and 'PartitionDate' of datetime type.

Returns

Type Description
Dataset

Dataset object.

Remarks

Use this method to read Excel files in .xlsx format. Data can be read from one sheet in each Excel file. After creating a Dataset, you should use get_profile to list detected column types and summary statistics for each column. The returned Dataset is not registered with the workspace.

from_json_files

Create an unregistered, in-memory Dataset from JSON files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_json_lines_files instead to read from JSON lines file. For more information, see https://aka.ms/dataset-deprecation.

static from_json_files(path, encoding=FileEncoding.UTF8, flatten_nested_arrays=False, include_path=False, partition_format=None)

Parameters

Name Description
path
Required
DataReference or str

The path to the file(s) or folder(s) that you want to load and parse. It can either be a local path or an Azure Blob url. Globbing is supported. For example, you can use path = "./data*" to read all files with name starting with "data".
encoding
Required
FileEncoding

The encoding of the files being read.
flatten_nested_arrays
Required
bool

Property controlling program's handling of nested arrays. If you choose to flatten nested JSON arrays, it could result in a much larger number of rows.
include_path
Required
bool

Whether to include a column containing the path from which the data was read. This is useful when you are reading multiple files, and might want to know which file a particular record originated from, or to keep useful information in file path.
partition_format
Required
str

Specify the partition format in path and create string columns from format '{x}' and datetime column from format '{x:yyyy/MM/dd/HH/mm/ss}', where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extrat year, month, day, hour, minute and second for the datetime type. The format should start from the postition of first partition key until the end of file path. For example, given a file path '../Accounts/2019/01/01/data.json' and data is partitioned by department name and time, we can define '/{Department}/{PartitionDate:yyyy/MM/dd}/data.json' to create columns 'Department' of string type and 'PartitionDate' of datetime type.

Returns

Type Description
Dataset

The local Dataset object.

from_pandas_dataframe

Create an unregistered, in-memory Dataset from a pandas dataframe.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.register_pandas_dataframe instead. For more information, see https://aka.ms/dataset-deprecation.

static from_pandas_dataframe(dataframe, path=None, in_memory=False)

Parameters

Name Description
dataframe
Required
DataFrame

The Pandas DataFrame.
path
Required
Union[DataReference, str]

A data path in registered datastore or local folder path.
in_memory
Required
bool

Whether to read the DataFrame from memory instead of persisting to disk.

Returns

Type Description
Dataset

A Dataset object.

Remarks

Use this method to convert a Pandas dataframe to a Dataset object. A Dataset created by this method can not be registered, as the data is from memory.

If in_memory is False, the Pandas DataFrame is converted to a CSV file locally. If pat is of type DataReference, then the Pandas frame will be uploaded to the data store, and the Dataset will be based off the DataReference. If ``path` is a local folder, the Dataset will be created off of the local file which cannot be deleted.

Raises an exception if the current DataReference is not a folder path.

from_parquet_files

Create an unregistered, in-memory Dataset from parquet files.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_parquet_files instead. For more information, see https://aka.ms/dataset-deprecation.

static from_parquet_files(path, include_path=False, partition_format=None)

Parameters

Name Description
path
Required
DataReference or str

A data path in a registered datastore or a local path.
include_path
Required
bool

Whether to include a column containing the path of the file from which the data was read. This is useful when you are reading multiple files, and want to know which file a particular record originated from, or to keep useful information in file path.
partition_format
Required
str

Specify the partition format in path and create string columns from format '{x}' and datetime column from format '{x:yyyy/MM/dd/HH/mm/ss}', where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extrat year, month, day, hour, minute and second for the datetime type. The format should start from the postition of first partition key until the end of file path. For example, given a file path '../Accounts/2019/01/01/data.parquet' where data is partitioned by department name and time, we can define '/{Department}/{PartitionDate:yyyy/MM/dd}/data.parquet' to create columns 'Department' of string type and 'PartitionDate' of datetime type.

Returns

Type Description
Dataset

Dataset object.

Remarks

Use this method to read Parquet files.

After creating a Dataset, you should use get_profile to list detected column types and summary statistics for each column.

The returned Dataset is not registered with the workspace.

from_sql_query

Create an unregistered, in-memory Dataset from a SQL query.

Note

This method is deprecated and will no longer be supported.

Recommend to use Dataset.Tabular.from_sql_query instead. For more information, see https://aka.ms/dataset-deprecation.

static from_sql_query(data_source, query)

Parameters

Name Description
data_source
Required
AzureSqlDatabaseDatastore

The details of the Azure SQL datastore.
query
Required
str

The query to execute to read data.

Returns

Type Description
Dataset

The local Dataset object.

generate_profile

Generate new profile for the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

generate_profile(compute_target=None, workspace=None, arguments=None)

Parameters

Name Description
compute_target
Required
Union[ComputeTarget, str]

An optional compute target to perform the snapshot profile creation. If omitted, the local compute is used.
workspace
Required
Workspace

Workspace, required for transient(unregistered) Datasets.
arguments
Required
dict[str, object]

Profile arguments. Valid arguments are:

  • 'include_stype_counts' of type bool. Check if values look like some well known semantic types such as email address, IP Address (V4/V6), US phone number, US zipcode, Latitude/Longitude. Enabling this impacts performance.

  • 'number_of_histogram_bins' of type int. Represents the number of histogram bins to use for numeric data. The default value is 10.

Returns

Type Description
DatasetActionRun

Dataset action run object.

Remarks

Synchronous call, will block till it completes. Call get_result to get the result of the action.

get

Get a Dataset that already exists in the workspace by specifying either its name or ID.

Note

This method is deprecated and will no longer be supported.

Recommend to use get_by_name and get_by_id instead. For more information, see https://aka.ms/dataset-deprecation.

static get(workspace, name=None, id=None)

Parameters

Name Description
workspace
Required
Workspace

The existing AzureML workspace in which the Dataset was created.
name
Required
str

The name of the Dataset to be retrieved.
id
Required
str

A unique identifier of the Dataset in the workspace.

Returns

Type Description
Dataset

The Dataset with the specified name or ID.

Remarks

You can provide either name or id. An exception is raised if:

  • both name and id are specified but don't match.

  • the Dataset with the specified name or id cannot be found in the workspace.

get_all

Get all the registered datasets in the workspace.

get_all()

Parameters

Name Description
workspace
Required
Workspace

The existing AzureML workspace in which the Datasets were registered.

Returns

Type Description
dict[str, Union[TabularDataset, FileDataset]]

A dictionary of TabularDataset and FileDataset objects keyed by their registration name.

get_all_snapshots

Get all snapshots of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

get_all_snapshots()

Returns

Type Description
list[DatasetSnapshot]

List of Dataset snapshots.

get_by_id

Get a Dataset which is saved to the workspace.

get_by_id(id, **kwargs)

Parameters

Name Description
workspace
Required
Workspace

The existing AzureML workspace in which the Dataset is saved.
id
Required
str

The id of dataset.

Returns

Type Description
Union[TabularDataset, FileDataset]

The dataset object. If dataset is registered, its registration name and version will also be returned.

get_by_name

Get a registered Dataset from workspace by its registration name.

get_by_name(name, version='latest', **kwargs)

Parameters

Name Description
workspace
Required
Workspace

The existing AzureML workspace in which the Dataset was registered.
name
Required
str

The registration name.
version
Required
int

The registration version. Defaults to 'latest'.

Returns

Type Description
Union[TabularDataset, FileDataset]

The registered dataset object.

get_definition

Get a specific definition of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

get_definition(version_id=None)

Parameters

Name Description
version_id
Required
str

The version ID of the Dataset definition

Returns

Type Description
DatasetDefinition

The Dataset definition.

Remarks

If version_id is provided, then Azure Machine Learning tries to get the definition corresponding to that version. If that version does not exist, an exception is thrown. If version_id is omitted, then the latest version is retrieved.

get_definitions

Get all the definitions of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

get_definitions()

Returns

Type Description
dict[str, DatasetDefinition]

A dictionary of Dataset definitions.

Remarks

A Dataset registered in an AzureML workspace can have multiple definitions, each created by calling update_definition. Each definition has an unique identifier. The current definition is the latest one created.

For unregistered Datasets, only one definition exists.

get_profile

Get summary statistics on the Dataset computed earlier.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

get_profile(arguments=None, generate_if_not_exist=True, workspace=None, compute_target=None)

Parameters

Name Description
arguments
Required
dict[str, object]

Profile arguments.
generate_if_not_exist
Required
bool

Indicates whether to generate a profile if it doesn't exist.
workspace
Required
Workspace

Workspace, required for transient(unregistered) Datasets.
compute_target
Required
Union[ComputeTarget, str]

A compute target to execute the profile action.

Returns

Type Description
<xref:azureml.dataprep.DataProfile>

DataProfile of the Dataset.

Remarks

For a Dataset registered with an Azure Machine Learning workspace, this method retrieves an existing profile that was created earlier by calling get_profile if it is still valid. Profiles are invalidated when changed data is detected in the Dataset or the arguments to get_profile are different from the ones used when the profile was generated. If the profile is not present or invalidated, generate_if_not_exist will determine if a new profile is generated.

For a Dataset that is not registered with an Azure Machine Learning workspace, this method always runs generate_profile and returns the result.

get_snapshot

Get snapshot of the Dataset by name.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

get_snapshot(snapshot_name)

Parameters

Name Description
snapshot_name
Required
str

The snapshot name.

Returns

Type Description
DatasetSnapshot

Dataset snapshot object.

head

Pull the specified number of records specified from this Dataset and returns them as a DataFrame.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

head(count)

Parameters

Name Description
count
Required
int

The number of records to pull.

Returns

Type Description
DataFrame

A Pandas DataFrame.

list

List all the Datasets in the workspace, including ones with is_visible property equal to False.

Note

This method is deprecated and will no longer be supported.

Recommend to use get_all instead. For more information, see https://aka.ms/dataset-deprecation.

static list(workspace)

Parameters

Name Description
workspace
Required
Workspace

The workspace for which you want to retrieve the list of Datasets.

Returns

Type Description
list[Dataset]

A list of Dataset objects.

reactivate

Reactivate an archived or deprecated dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

reactivate()

Returns

Type Description
None

None.

register

Register the Dataset in the workspace, making it available to other users of the workspace.

Note

This method is deprecated and will no longer be supported.

Recommend to use register instead. For more information, see https://aka.ms/dataset-deprecation.

register(workspace, name, description=None, tags=None, visible=True, exist_ok=False, update_if_exist=False)

Parameters

Name Description
workspace
Required
Workspace

The AzureML workspace in which the Dataset is to be registered.
name
Required
str

The name of the Dataset in the workspace.
description
Required
str

A description of the Dataset.
tags
Required
dict[str, str]

Tags to associate with the Dataset.
visible
Required
bool

Indicates whether the Dataset is visible in the UI. If False, then the Dataset is hidden in the UI and available via SDK.
exist_ok
Required
bool

If True, the method returns the Dataset if it already exists in the given workspace, else error.
update_if_exist
Required
bool

If exist_ok is True and update_if_exist is True, this method will update the definition and return the updated Dataset.

Returns

Type Description
Dataset

A registered Dataset object in the workspace.

sample

Generate a new sample from the source Dataset, using the sampling strategy and parameters provided.

Note

This method is deprecated and will no longer be supported.

Create a TabularDataset by calling the static methods on Dataset.Tabular and use the take_sample method there. For more information, see https://aka.ms/dataset-deprecation.

sample(sample_strategy, arguments)

Parameters

Name Description
sample_strategy
Required
str

Sample strategy to use. Accepted values are "top_n", "simple_random", or "stratified".
arguments
Required
dict[str, object]

A dictionary with keys from the "Optional argument" in the list shown above, and values from tye "Type" column. Only arguments from the corresponding sampling method can be used. For example, for a "simple_random" sample type, you can only specify a dictionary with "probability" and "seed" keys.

Returns

Type Description
Dataset

Dataset object as a sample of the original dataset.

Remarks

Samples are generated by executing the transformation pipeline defined by this Dataset, and then applying the sampling strategy and parameters to the output data. Each sampling method supports the following optional arguments:

  • top_n

    • Optional arguments

      • n, type integer. Select top N rows as your sample.

  • simple_random

    • Optional arguments

      • probability, type float. Simple random sampling where each row has equal probability of being selected. Probability should be a number between 0 and 1.

      • seed, type float. Used by random number generator. Use for repeatability.

  • stratified

    • Optional arguments

      • columns, type list[str]. List of strata columns in the data.

      • seed, type float. Used by random number generator. Use for repeatability.

      • fractions, type dict[tuple, float]. Tuple: column values that define a stratum, must be in the same order as column names. Float: weight attached to a stratum during sampling.

The following code snippets are example design patterns for different sample methods.


   # sample_strategy "top_n"
   top_n_sample_dataset = dataset.sample('top_n', {'n': 5})

   # sample_strategy "simple_random"
   simple_random_sample_dataset = dataset.sample('simple_random', {'probability': 0.3, 'seed': 10.2})

   # sample_strategy "stratified"
   fractions = {}
   fractions[('THEFT',)] = 0.5
   fractions[('DECEPTIVE PRACTICE',)] = 0.2

   # take 50% of records with "Primary Type" as THEFT and 20% of records with "Primary Type" as
   # DECEPTIVE PRACTICE into sample Dataset
   sample_dataset = dataset.sample('stratified', {'columns': ['Primary Type'], 'fractions': fractions})

to_pandas_dataframe

Create a Pandas dataframe by executing the transformation pipeline defined by this Dataset definition.

Note

This method is deprecated and will no longer be supported.

Create a TabularDataset by calling the static methods on Dataset.Tabular and use the to_pandas_dataframe method there. For more information, see https://aka.ms/dataset-deprecation.

to_pandas_dataframe()

Returns

Type Description
DataFrame

A Pandas DataFrame.

Remarks

Return a Pandas DataFrame fully materialized in memory.

to_spark_dataframe

Create a Spark DataFrame that can execute the transformation pipeline defined by this Dataset definition.

Note

This method is deprecated and will no longer be supported.

Create a TabularDataset by calling the static methods on Dataset.Tabular and use the to_spark_dataframe method there. For more information, see https://aka.ms/dataset-deprecation.

to_spark_dataframe()

Returns

Type Description
DataFrame

A Spark DataFrame.

Remarks

The Spark Dataframe returned is only an execution plan and does not actually contain any data, as Spark Dataframes are lazily evaluated.

update

Update the Dataset mutable attributes in the workspace and return the updated Dataset from the workspace.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

update(name=None, description=None, tags=None, visible=None)

Parameters

Name Description
name
Required
str

The name of the Dataset in the workspace.
description
Required
str

A description of the data.
tags
Required
dict[str, str]

Tags to associate the Dataset with.
visible
Required
bool

Indicates whether the the Dataset is visible in the UI.

Returns

Type Description
Dataset

An updated Dataset object from the workspace.

update_definition

Update the Dataset definition.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

update_definition(definition, definition_update_message)

Parameters

Name Description
definition
Required
DatasetDefinition

The new definition of this Dataset.
definition_update_message
Required
str

The definition update message.

Returns

Type Description
Dataset

An updated Dataset object from the workspace.

Remarks

To consume the updated Dataset, use the object returned by this method.

Attributes

definition

Return the current Dataset definition.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

Returns

Type Description
DatasetDefinition

The Dataset definition.

Remarks

A Dataset definition is a series of steps that specify how to read and transform data.

A Dataset registered in an AzureML workspace can have multiple definitions, each created by calling update_definition. Each definition has an unique identifier. Having multiple definitions allows you to make changes to existing Datasets without breaking models and pipelines that depend on the older definition.

For unregistered Datasets, only one definition exists.

definition_version

Return the version of the current definition of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

Returns

Type Description
str

The Dataset definition version.

Remarks

A Dataset definition is a series of steps that specify how to read and transform data.

A Dataset registered in an AzureML workspace can have multiple definitions, each created by calling update_definition. Each definition has an unique identifier. The current definition is the latest one created, whose ID is returned by this.

For unregistered Datasets, only one definition exists.

description

Return the description of the Dataset.

Returns

Type Description
str

The Dataset description.

Remarks

Specifying a description of the data in the Dataset enables users of the workspace to understand what the data represents, and how they can use it.

id

If the Dataset was registered in a workspace, return the ID of the Dataset. Otherwise, return None.

Returns

Type Description
str

The Dataset ID.

is_visible

Control the visibility of a registered Dataset in the Azure ML workspace UI.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

Returns

Type Description
bool

The Dataset visibility.

Remarks

Values returned:

  • True: Dataset is visible in workspace UI. Default.

  • False: Dataset is hidden in workspace UI.

Has no effect on unregistered Datasets.

name

Return the Dataset name.

Returns

Type Description
str

The Dataset name.

state

Return the state of the Dataset.

Note

This method is deprecated and will no longer be supported.

For more information, see https://aka.ms/dataset-deprecation.

Returns

Type Description
str

The Dataset state.

Remarks

The meaning and effect of states are as follows:

  • Active. Active definitions are exactly what they sound like, all actions can be performed on active definitions.

  • Deprecated. deprecated definition can be used, but will result in a warning being logged in the logs everytime the underlying data is accessed.

  • Archived. An archived definition cannot be used to perform any action. To perform actions on an archived definition, it must be reactivated.

tags

Return the tags associated with the Dataset.

Returns

Type Description
dict[str, str]

Dataset tags.

workspace

If the Dataset was registered in a workspace, return that. Otherwise, return None.

Returns

Type Description
Workspace

The workspace.

Tabular

Factory for creating FileDataset

alias of TabularDatasetFactory