MLTable Class

Represents a MLTable.

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

Initialize a new MLTable.

This constructor is not supposed to be invoked directly. MLTable is intended to be created using load.

Inheritance

builtins.object

MLTable

Constructor

MLTable()

Methods

convert_column_types	Adds a transformation step to convert the specified columns into their respective specified new types. from mltable import DataType data_types = { 'ID': DataType.to_string(), 'Date': DataType.to_datetime('%d/%m/%Y %I:%M:%S %p'), 'Count': DataType.to_int(), 'Latitude': DataType.to_float(), 'Found': DataType.to_bool(), 'Stream': DataType.to_stream() }
drop_columns	Adds a transformation step to drop the given columns from the dataset. If an empty list, tuple, or set is given nothing is dropped. Duplicate columns will raise a UserErrorException. Attempting to drop a column that is MLTable.traits.timestamp_column or in MLTable.traits.index_columns will raise a UserErrorException.
extract_columns_from_partition_format	Adds a transformation step to use the partition information of each path and extract them into columns based on the specified partition format. Format part '{column_name}' creates string column, and '{column_name:yyyy/MM/dd/HH/mm/ss}' creates datetime column, where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extract year, month, day, hour, minute and second for the datetime type. The format should start from the position of first partition key until the end of file path. For example, given the path '/Accounts/2019/01/01/data.csv' where the partition is by department name and time, partition_format='/{Department}/{PartitionDate:yyyy/MM/dd}/data.csv' creates a string column 'Department' with the value 'Accounts' and a datetime column 'PartitionDate' with the value '2019-01-01'.
filter	Filter the data, leaving only the records that match the specified expression.
get_partition_count	Returns the number of data partitions underlying the data associated with this MLTable.
keep_columns	Adds a transformation step to keep the specified columns and drop all others from the dataset. If an empty list, tuple, or set is given nothing is dropped. Duplicate columns will raise a UserErrorException. If column in MLTable.traits.timestamp_column or columns in MLTable.traits.index_columns are not explicitly kept, a UserErrorException is raiesd.
random_split	Randomly splits this MLTable into two MLTables, one having approximately "percent"% of the original MLTable's data and the other having the remainder (1-"percent"%).
save	Save this MLTable as a MLTable YAML file & its assoicated paths to the given directory path. If path is not given, defaults to the current working directory. If path does not exist, it is created. If path is remote, the underlying data store must already exist. If path is a local directory & is not absolute, it is made absolute. If path points to a file, a UserErrorException is raised. If path is a directory path that already contain one or more files being saved (including the MLTable YAML file) and overwrite is set to False or 'fail' - a UserErrorException is raised. If path is remote, any local files paths not given as a colocated path (file path relative to the directory that MLTable was loaded from) will raise a UserErrorException. colocated controls how associated paths are saved to path. If True, files are copied to path alongside the MLTable YAML file as relative file paths. Otherwise associated files are not copied, remote paths remain as given and local file paths are made relative with path redirection if needed. Note that False may result in noncolocated MLTable YAML files which is not recommended, furthermore if path is remote this will result in a UserErrorException as relative path redirection is not supported for remote URIs. Note that if the MLTable is created programatically with methods like from_paths() or from_read_delimited_files() with local relative paths, the MLTable directory path is assumed to be the current working directory. Be mindful when saving a new MLTable & associated data files to a directory with an existing MLTable file & associated data files that the directory is not cleared of existing files before saving the new files. It is possible for already existing data files to persist after saving the new files, especially if existing data files do not have names matching any new data files. If the new MLTable contains a pattern designator under its paths, this may unintentionally alter the MLTable by associating existing data files with the new MLTable. If file paths in this MLTable point to an existing file in path but have a different URI, if overwrite is 'fail' or 'skip' the existing file will not be overwritten (i.e. skipped).
select_partitions	Adds a transformation step to select the partition.
show	Retrieves the first count rows of this MLTable as a Pandas Dataframe.
skip	Adds a transformation step to skip the first count rows of this MLTable.
take	Adds a transformation step to select the first count rows of this MLTable.
take_random_sample	Adds a transformation step to randomly select each row of this MLTable with probability chance. Probability must be in range [0, 1]. May optionally set a random seed.
to_pandas_dataframe	Load all records from the paths specified in the MLTable file into a Pandas DataFrame.
validate	Validates if this MLTable's data can be loaded, requires the MLTable's data source(s) to be accessible from the current compute.

convert_column_types

Adds a transformation step to convert the specified columns into their respective specified new types.

   from mltable import DataType
       data_types = {
           'ID': DataType.to_string(),
           'Date': DataType.to_datetime('%d/%m/%Y %I:%M:%S %p'),
           'Count': DataType.to_int(),
           'Latitude': DataType.to_float(),
           'Found': DataType.to_bool(),
           'Stream': DataType.to_stream()
       }

convert_column_types(column_types)

Parameters

Name	Description
column_types Required	dict[Union[Tuple[str], str], DataType] Dictionary of column: types the user desires to convert

Returns

Type	Description
MLTable	MLTable with added transformation step

drop_columns

Adds a transformation step to drop the given columns from the dataset. If an empty list, tuple, or set is given nothing is dropped. Duplicate columns will raise a UserErrorException.

Attempting to drop a column that is MLTable.traits.timestamp_column or in MLTable.traits.index_columns will raise a UserErrorException.

drop_columns(columns: str | List[str] | Tuple[str] | Set[str])

Parameters

Name	Description
columns Required	Union[str, list[str], <xref:builtin.tuple>[str], <xref:builtin.set>[str]] column(s) to drop from this MLTable

Returns

Type	Description
MLTable	MLTable with added transformation step

extract_columns_from_partition_format

Adds a transformation step to use the partition information of each path and extract them into columns based on the specified partition format.

Format part '{column_name}' creates string column, and '{column_name:yyyy/MM/dd/HH/mm/ss}' creates datetime column, where 'yyyy', 'MM', 'dd', 'HH', 'mm' and 'ss' are used to extract year, month, day, hour, minute and second for the datetime type.

The format should start from the position of first partition key until the end of file path. For example, given the path '/Accounts/2019/01/01/data.csv' where the partition is by department name and time, partition_format='/{Department}/{PartitionDate:yyyy/MM/dd}/data.csv' creates a string column 'Department' with the value 'Accounts' and a datetime column 'PartitionDate' with the value '2019-01-01'.

extract_columns_from_partition_format(partition_format)

Parameters

Name	Description
partition_format Required	str Partition format to use to extract data into columns

Returns

Type	Description
MLTable	MLTable whose partition format is set to given format

filter

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

filter(expression)

Parameters

Name	Description
expression Required	string The expression to evaluate.

Returns

Type	Description
MLTable	MLTable after filter

Remarks

Expressions are started by indexing the mltable 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.

   filtered_mltable = mltable.filter('feature_1 == "5" and target > "0.5)"')
   filtered_mltable = mltable.filter('col("FBI Code") == "11"')

get_partition_count

Returns the number of data partitions underlying the data associated with this MLTable.

get_partition_count() -> int

Returns

Type	Description
int	data partitions in this MLTable

keep_columns

Adds a transformation step to keep the specified columns and drop all others from the dataset. If an empty list, tuple, or set is given nothing is dropped. Duplicate columns will raise a UserErrorException.

If column in MLTable.traits.timestamp_column or columns in MLTable.traits.index_columns are not explicitly kept, a UserErrorException is raiesd.

keep_columns(columns: str | List[str] | Tuple[str] | Set[str])

Parameters

Name	Description
columns Required	Union[str, list[str], <xref:builtin.tuple>[str], <xref:builtin.set>[str]] columns(s) in this MLTable to keep

Returns

Type	Description
MLTable	MLTable with added transformation step

random_split

Randomly splits this MLTable into two MLTables, one having approximately "percent"% of the original MLTable's data and the other having the remainder (1-"percent"%).

random_split(percent=0.5, seed=None)

Parameters

Name	Description
percent Required	Union[int, float] percent of the MLTable to split between
seed Required	Optional[int] optional random seed

Returns

Type	Description
Tuple[MLTable, MLTable]	two MLTables with this MLTable's data split between them by "percent"

save

Save this MLTable as a MLTable YAML file & its assoicated paths to the given directory path.

If path is not given, defaults to the current working directory. If path does not exist, it is created. If path is remote, the underlying data store must already exist. If path is a local directory & is not absolute, it is made absolute.

If path points to a file, a UserErrorException is raised. If path is a directory path that already contain one or more files being saved (including the MLTable YAML file) and overwrite is set to False or 'fail' - a UserErrorException is raised. If path is remote, any local files paths not given as a colocated path (file path relative to the directory that MLTable was loaded from) will raise a UserErrorException.

colocated controls how associated paths are saved to path. If True, files are copied to path alongside the MLTable YAML file as relative file paths. Otherwise associated files are not copied, remote paths remain as given and local file paths are made relative with path redirection if needed. Note that False may result in noncolocated MLTable YAML files which is not recommended, furthermore if path is remote this will result in a UserErrorException as relative path redirection is not supported for remote URIs.

Note that if the MLTable is created programatically with methods like from_paths() or from_read_delimited_files() with local relative paths, the MLTable directory path is assumed to be the current working directory.

Be mindful when saving a new MLTable & associated data files to a directory with an existing MLTable file & associated data files that the directory is not cleared of existing files before saving the new files. It is possible for already existing data files to persist after saving the new files, especially if existing data files do not have names matching any new data files. If the new MLTable contains a pattern designator under its paths, this may unintentionally alter the MLTable by associating existing data files with the new MLTable.

If file paths in this MLTable point to an existing file in path but have a different URI, if overwrite is 'fail' or 'skip' the existing file will not be overwritten (i.e. skipped).

save(path=None, overwrite=True, colocated=False, show_progress=False, if_err_remove_files=True)

Parameters

Name	Description
path Required	str directory path to save to, default to current working directory
colocated Required	bool If True, saves copies of local & remote file paths in this MLTable under path as relative paths. Otherwise no file copying occurs and remote file paths are saved as given to the saved MLTable YAML file and local file paths as relative file paths with path redirection. If path is remote & this MLTable contains local file paths, a UserErrorException will be raised.
overwrite Required	Union[bool, str, <xref:mltable.MLTableSaveOverwriteOptions>] How existing an MLTable YAML file and associated files that may already exist under path are handled. Options are 'overwrite' (or True) to replace any existing files, 'fail' (or False) to raise an error if a file already exists, or 'skip' to leave existing files as is. May also set with <xref:mltable.MLTableSaveOverwriteOptions>.
show_progress Required	bool displays copying progress to stdout
if_err_remove_files Required	bool if any error occurs during saving, removed any successfully saved files to make the operation atomic

Returns

Type	Description
MLTable	this MLTable instance

select_partitions

Adds a transformation step to select the partition.

select_partitions(partition_index_list)

Parameters

Name	Description
partition_index_list Required	list of int list of partition index

Returns

Type	Description
MLTable	MLTable with partition size updated

Remarks

The following code snippet shows how to use the select_partitions api to selected partitions from the provided MLTable.

   partition_index_list = [1, 2]
   mltable = mltable.select_partitions(partition_index_list)

show

Retrieves the first count rows of this MLTable as a Pandas Dataframe.

show(count=20)

Parameters

Name	Description
count Required	int number of rows from top of table to select

Returns

Type	Description
<xref:Pandas> <xref:Dataframe>	first count rows of the MLTable

skip

Adds a transformation step to skip the first count rows of this MLTable.

skip(count)

Parameters

Name	Description
count Required	int number of rows to skip

Returns

Type	Description
	MLTable with added transformation step

take

Adds a transformation step to select the first count rows of this MLTable.

take(count=20)

Parameters

Name	Description
count Required	int number of rows from top of table to select

Returns

Type	Description
MLTable	MLTable with added "take" transformation step

take_random_sample

Adds a transformation step to randomly select each row of this MLTable with probability chance. Probability must be in range [0, 1]. May optionally set a random seed.

take_random_sample(probability, seed=None)

Parameters

Name	Description
probability Required	chance that each row is selected
seed Required	Optional[int] optional random seed

Returns

Type	Description
MLTable	MLTable with added transformation step

to_pandas_dataframe

Load all records from the paths specified in the MLTable file into a Pandas DataFrame.

to_pandas_dataframe()

Returns

Type	Description
DataFrame	Pandas Dataframe containing the records from paths in this MLTable

Remarks

The following code snippet shows how to use the to_pandas_dataframe api to obtain a pandas dataframe corresponding to the provided MLTable.

   from mltable import load
   tbl = load('.\samples\mltable_sample')
   pdf = tbl.to_pandas_dataframe()
   print(pdf.shape)

validate

Validates if this MLTable's data can be loaded, requires the MLTable's data source(s) to be accessible from the current compute.

validate()

Returns

Type	Description
None	None

Attributes

partition_keys

Return the partition keys.

Returns

Type	Description
list[str]	the partition keys

paths

Returns a list of dictionaries containing the original paths given to this MLTable. Relative local file paths are assumed to be relative to the directory where the MLTable YAML file this MLTable instance was loaded from.

Returns

Type	Description
list[dict[str, str]]	list of dicts containing paths specified in the MLTable