TabularDataset Class

Represents a tabular dataset to use in Azure Machine Learning.

A TabularDataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation. Data is not loaded from the source until TabularDataset is asked to deliver data.

TabularDataset is created using methods like from_delimited_files from the TabularDatasetFactory class.

For more information, see the article Add & register datasets. To get started working with a tabular dataset, see https://aka.ms/tabulardataset-samplenotebook.

Inheritance
TabularDataset

Constructor

TabularDataset()

Remarks

A TabularDataset can be created from CSV, TSV, Parquet files, or SQL query using the from_* methods of the TabularDatasetFactory class. You can perform subsetting operations on a TabularDataset like splitting, skipping, and filtering records. The result of subsetting is always one or more new TabularDataset objects.

You can also convert a TabularDataset into other formats like a pandas DataFrame. The actual data loading happens when TabularDataset is asked to deliver the data into another storage mechanism (e.g. a Pandas Dataframe, or a CSV file).

TabularDataset can be used as input of an experiment run. It can also be registered to workspace with a specified name and be retrieved by that name later.

Methods

download

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Download file streams defined by the dataset to local path.

drop_columns

Drop the specified columns from the dataset.

If a timeseries column is dropped, the corresponding capabilities will be dropped for the returned dataset as well.

filter

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Filter the data, leaving only the records that match the specified expression.

get_profile

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Get data profile from the latest profile run submitted for this or the same dataset in the workspace.

get_profile_runs

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Return previous profile runs associated with this or same dataset in the workspace.

keep_columns

Keep the specified columns and drops all others from the dataset.

If a timeseries column is dropped, the corresponding capabilities will be dropped for the returned dataset as well.

mount

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Create a context manager for mounting file streams defined by the dataset as local files.

partition_by

Partitioned data will be copied and output to the destination specified by target.

create the dataset from the outputted data path with partition format, register dataset if name is provided, return the dataset for the new data path with partitions


   ds = Dataset.get_by_name('test') # indexed by country, state, partition_date

   # #1: call partition_by locally
   new_ds = ds.partition_by(name="repartitioned_ds", partition_keys=['country'],
               target=DataPath(datastore, "repartition"))
   partition_keys = newds.partition_keys # ['country']

   # new_ds can be passed to PRS as input dataset
random_split

Split records in the dataset into two parts randomly and approximately by the percentage specified.

The first dataset contains approximately percentage of the total records and the second dataset the remaining records.

skip

Skip records from top of the dataset by the specified count.

submit_profile_run

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Submit an experimentation run to calculate data profile.

A data profile can be very useful to understand the input data, identify anomalies and missing values by providing useful information about the data like column type, missing values, etc.

take

Take a sample of records from top of the dataset by the specified count.

take_sample

Take a random sample of records in the dataset approximately by the probability specified.

time_after

Filter TabularDataset with time stamp columns after a specified start time.

time_before

Filter TabularDataset with time stamp columns before a specified end time.

time_between

Filter TabularDataset between a specified start and end time.

time_recent

Filter TabularDataset to contain only the specified duration (amount) of recent data.

to_csv_files

Convert the current dataset into a FileDataset containing CSV files.

The resulting dataset will contain one or more CSV files, each corresponding to a partition of data from the current dataset. These files are not materialized until they are downloaded or read from.

to_dask_dataframe

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Return a Dask DataFrame that can lazily read the data in the dataset.

to_pandas_dataframe

Load all records from the dataset into a pandas DataFrame.

to_parquet_files

Convert the current dataset into a FileDataset containing Parquet files.

The resulting dataset will contain one or more Parquet files, each corresponding to a partition of data from the current dataset. These files are not materialized until they are downloaded or read from.

to_spark_dataframe

Load all records from the dataset into a Spark DataFrame.

with_timestamp_columns

Define timestamp columns for the dataset.

download

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Download file streams defined by the dataset to local path.

download(stream_column, target_path=None, overwrite=False, ignore_not_found=True)

Parameters

stream_column
str
Required

The stream column to download.

target_path
str
Required

The local directory to download the files to. If None, the data will be downloaded into a temporary directory.

overwrite
bool
Required

Indicates whether to overwrite existing files. The default is False. Existing files will be overwritten if overwrite is set to True; otherwise an exception will be raised.

ignore_not_found
bool
Required

Indicates whether to fail download if some files pointed to by dataset are not found. The default is True. Download will fail if any file download fails for any reason if ignore_not_found is set to False; otherwise a waring will be logged for not found errors and dowload will succeed as long as no other error types are encountered.

Returns

Returns an array of file paths for each file downloaded.

Return type

drop_columns

Drop the specified columns from the dataset.

If a timeseries column is dropped, the corresponding capabilities will be dropped for the returned dataset as well.

drop_columns(columns)

Parameters

columns
Union[str, list[str]]
Required

The name or a list of names for the columns to drop.

Returns

Returns a new TabularDataset object with the specified columns dropped.

Return type

filter

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Filter the data, leaving only the records that match the specified expression.

filter(expression)

Parameters

expression
any
Required

The expression to evaluate.

Returns

The modified dataset (unregistered).

Return type

Remarks

Expressions are started by indexing the Dataset with the name of a column. They support a variety of functions and operators and can be combined using logical operators. The resulting expression will be lazily evaluated for each record when a data pull occurs and not where it is defined.


   dataset['myColumn'] > dataset['columnToCompareAgainst']
   dataset['myColumn'].starts_with('prefix')

get_profile

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Get data profile from the latest profile run submitted for this or the same dataset in the workspace.

get_profile(workspace=None)

Parameters

workspace
Workspace
Required

The workspace where profile run was submitted. Defaults to the workspace of this dataset. Required if dataset is not associated to a workspace. See https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace for more information on workspaces.

Returns

Profile result from the latest profile run of type DatasetProfile.

Return type

get_profile_runs

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Return previous profile runs associated with this or same dataset in the workspace.

get_profile_runs(workspace=None)

Parameters

workspace
Workspace
Required

The workspace where profile run was submitted. Defaults to the workspace of this dataset. Required if dataset is not associated to a workspace. See https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace for more information on workspaces.

Returns

iterator object of type azureml.core.Run.

Return type

keep_columns

Keep the specified columns and drops all others from the dataset.

If a timeseries column is dropped, the corresponding capabilities will be dropped for the returned dataset as well.

keep_columns(columns, validate=False)

Parameters

columns
Union[str, list[str]]
Required

The name or a list of names for the columns to keep.

validate
bool
Required

Indicates whether to validate if data can be loaded from the returned dataset. The default is False. Validation requires that the data source is accessible from current compute.

Returns

Returns a new TabularDataset object with only the specified columns kept.

Return type

mount

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Create a context manager for mounting file streams defined by the dataset as local files.

mount(stream_column, mount_point=None)

Parameters

stream_column
str
Required

The stream column to mount.

mount_point
str
Required

The local directory to mount the files to. If None, the data will be mounted into a temporary directory, which you can find by calling the MountContext.mount_point instance method.

Returns

Returns a context manager for managing the lifecycle of the mount.

Return type

<xref:azureml.dataprep.fuse.daemon.MountContext>

partition_by

Partitioned data will be copied and output to the destination specified by target.

create the dataset from the outputted data path with partition format, register dataset if name is provided, return the dataset for the new data path with partitions


   ds = Dataset.get_by_name('test') # indexed by country, state, partition_date

   # #1: call partition_by locally
   new_ds = ds.partition_by(name="repartitioned_ds", partition_keys=['country'],
               target=DataPath(datastore, "repartition"))
   partition_keys = newds.partition_keys # ['country']

   # new_ds can be passed to PRS as input dataset
partition_by(partition_keys, target, name=None, show_progress=True, partition_as_file_dataset=False)

Parameters

partition_keys
list[str]
Required

Required, partition keys

target
DataPath, Datastore or tuple(Datastore, str)object
Required

Required, the datastore path where the dataframe parquet data will be uploaded to. A guid folder will be generated under the target path to avoid conflict.

name
str
Required

Optional, The registration name.

show_progress
bool
Required

Optional, indicates whether to show progress of the upload in the console. Defaults to be True.

partition_as_file_dataset
Required

Optional, indicates whether returns a filedataset or not. Defaults to be False.

Returns

The saved or registered dataset.

Return type

random_split

Split records in the dataset into two parts randomly and approximately by the percentage specified.

The first dataset contains approximately percentage of the total records and the second dataset the remaining records.

random_split(percentage, seed=None)

Parameters

percentage
float
Required

The approximate percentage to split the dataset by. This must be a number between 0.0 and 1.0.

seed
int
Required

Optional seed to use for the random generator.

Returns

Returns a tuple of new TabularDataset objects representing the two datasets after the split.

Return type

skip

Skip records from top of the dataset by the specified count.

skip(count)

Parameters

count
int
Required

The number of records to skip.

Returns

Returns a new TabularDataset object representing a dataset with records skipped.

Return type

submit_profile_run

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Submit an experimentation run to calculate data profile.

A data profile can be very useful to understand the input data, identify anomalies and missing values by providing useful information about the data like column type, missing values, etc.

submit_profile_run(compute_target, experiment, cache_datastore_name=None)

Parameters

compute_target
Union[str, ComputeTarget]
Required

The compute target to run the profile calculation experiment on. Specify 'local' to use local compute. See https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.computetarget for more information on compute targets.

experiment
Experiment
Required

The experiment object. See https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment for more information on experiments.

cache_datastore_name
str
Required

the name of datastore to store the profile cache, if None, default datastore will be used

Returns

An object of type DatasetProfileRun class.

Return type

take

Take a sample of records from top of the dataset by the specified count.

take(count)

Parameters

count
int
Required

The number of records to take.

Returns

Returns a new TabularDataset object representing the sampled dataset.

Return type

take_sample

Take a random sample of records in the dataset approximately by the probability specified.

take_sample(probability, seed=None)

Parameters

probability
float
Required

The probability of a record being included in the sample.

seed
int
Required

Optional seed to use for the random generator.

Returns

Returns a new TabularDataset object representing the sampled dataset.

Return type

time_after

Filter TabularDataset with time stamp columns after a specified start time.

time_after(start_time, include_boundary=True, validate=True)

Parameters

start_time
datetime
Required

The lower bound for filtering data.

include_boundary
bool
Required

Indicate if the row associated with the boundary time (start_time) should be included.

validate
bool
Required

Indicates whether to validate if specified columns exist in dataset. The default is True. Validation requires that the data source is accessible from the current compute.

Returns

A TabularDataset with the new filtered dataset.

Return type

time_before

Filter TabularDataset with time stamp columns before a specified end time.

time_before(end_time, include_boundary=True, validate=True)

Parameters

end_time
datetime
Required

Upper bound for filtering data.

include_boundary
bool
Required

Indicate if the row associated with the boundary time (end_time) should be included.

validate
bool
Required

Indicates whether to validate if specified columns exist in dataset. The default is True. Validation requires that the data source is accessible from the current compute.

Returns

A TabularDataset with the new filtered dataset.

Return type

time_between

Filter TabularDataset between a specified start and end time.

time_between(start_time, end_time, include_boundary=True, validate=True)

Parameters

start_time
datetime
Required

The Lower bound for filtering data.

end_time
datetime
Required

The upper bound for filtering data.

include_boundary
bool
Required

Indicate if the row associated with the boundary time (start_end and end_time) should be included.

validate
bool
Required

Indicates whether to validate if specified columns exist in dataset. The default is True. Validation requires that the data source is accessible from the current compute.

Returns

A TabularDataset with the new filtered dataset.

Return type

time_recent

Filter TabularDataset to contain only the specified duration (amount) of recent data.

time_recent(time_delta, include_boundary=True, validate=True)

Parameters

time_delta
timedelta
Required

The duration (amount) of recent data to retrieve.

include_boundary
bool
Required

Indicate if the row associated with the boundary time (time_delta) should be included.

validate
bool
Required

Indicates whether to validate if specified columns exist in dataset. The default is True. Validation requires that the data source is accessible from the current compute.

Returns

A TabularDataset with the new filtered dataset.

Return type

to_csv_files

Convert the current dataset into a FileDataset containing CSV files.

The resulting dataset will contain one or more CSV files, each corresponding to a partition of data from the current dataset. These files are not materialized until they are downloaded or read from.

to_csv_files(separator=',')

Parameters

separator
str
Required

The separator to use to separate values in the resulting file.

Returns

Returns a new FileDataset object with a set of CSV files containing the data in this dataset.

Return type

to_dask_dataframe

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Return a Dask DataFrame that can lazily read the data in the dataset.

to_dask_dataframe(sample_size=10000, dtypes=None, on_error='null', out_of_range_datetime='null')

Parameters

sample_size
Required

The number of records to read to determine schema and types.

dtypes
Required

An optional dict specifying the expected columns and their dtypes. sample_size is ignored if this is provided.

on_error
Required

How to handle any error values in the dataset, such as those produced by an error while parsing values. Valid values are 'null' which replaces them with null; and 'fail' which will result in an exception.

out_of_range_datetime
Required

How to handle date-time values that are outside the range supported by Pandas. Valid values are 'null' which replaces them with null; and 'fail' which will result in an exception.

Returns

dask.dataframe.core.DataFrame

to_pandas_dataframe

Load all records from the dataset into a pandas DataFrame.

to_pandas_dataframe(on_error='null', out_of_range_datetime='null')

Parameters

on_error
Required

How to handle any error values in the dataset, such as those produced by an error while parsing values. Valid values are 'null' which replaces them with null; and 'fail' which will result in an exception.

out_of_range_datetime
Required

How to handle date-time values that are outside the range supported by Pandas. Valid values are 'null' which replaces them with null; and 'fail' which will result in an exception.

Returns

Returns a pandas DataFrame.

Return type

to_parquet_files

Convert the current dataset into a FileDataset containing Parquet files.

The resulting dataset will contain one or more Parquet files, each corresponding to a partition of data from the current dataset. These files are not materialized until they are downloaded or read from.

to_parquet_files()

Returns

Returns a new FileDataset object with a set of Parquet files containing the data in this dataset.

Return type

to_spark_dataframe

Load all records from the dataset into a Spark DataFrame.

to_spark_dataframe()

Returns

Returns a Spark DataFrame.

Return type

with_timestamp_columns

Define timestamp columns for the dataset.

with_timestamp_columns(timestamp=None, partition_timestamp=None, validate=False, **kwargs)

Parameters

timestamp
str
Required

The name of column as timestamp (used to be referred as fine_grain_timestamp) (optional). The default is None(clear).

partition_timestamp
str
Required

The name of column partition_timestamp (used to be referred as coarse grain timestamp) (optional). The default is None(clear).

validate
bool
Required

Indicates whether to validate if specified columns exist in dataset. The default is False. Validation requires that the data source is accessible from the current compute.

Returns

Returns a new TabularDataset with timestamp columns defined.

Return type

Remarks

The method defines columns to be used as timestamps. Timestamp columns on a dataset make it possible to treat the data as time-series data and enable additional capabilities. When a dataset has both timestamp (used to be referred as fine_grain_timestamp) and partition_timestamp (used to be referred as coarse grain timestamp) specified, the two columns should represent the same timeline.

Attributes

timestamp_columns

Return the timestamp columns.

Returns

The column names for timestamp (used to be referred as fine_grain_timestamp) and partition_timestamp (used to be referred as coarse grain timestamp) defined for the dataset.

Return type

(str, str)