`utils.rcclient.entities.dataset`

classmethod create(name, project_id, description, display_name, file_path, data_source_id, data_source_options, metadata, dataset_meta, force_upload, markdown, icon, image)

askai(query: str, chart_name: str = None)

add_markdown(markdown: str)

ensure_schema_download(save_to)

getData(num_rows=10, scenario_id=None, project_run_entry_id=None) → pandas.DataFrame

Get sample data of the dataset

Parameters:

num_rows (int, optional) – Number of rows to show, maximum can be 100.
data (Defaults to 10. pass -1 to get full)
scenario_id (string, optional)
project_run_entry_id (string, optional)

Returns:

pd.DataFrame

get_full_data(scenario_id=None, project_run_entry_id=None) → pandas.DataFrame

Get full data of the dataset

Parameters:

scenario_id (string, optional)
project_run_entry_id (string, optional)

Returns:

pd.DataFrame

getCols(scenario_id=None, project_run_entry_id=None) → List[str]

Get column names of the dataset

Returns:: List of all the column names
Return type:: List[str]

classmethod __deserialize_res(res_json, response_class)

refresh()

get_stats_status()

static getDataset(datasetId: str) → Dataset

Get dataset object using id of the dataset

Parameters:: datasetId (str) – id of the dataset
Return type:: Dataset

static deleteDataset(datasetId: str)

Delete the dataset using id of the dataset

Parameters:: datasetId (str) – id of the dataset

download_dataset(folder_path: str, project_run_entry_id: str = None, scenario_id: str = None, file_type: utils.rcclient.enums.DatasetFileType = FileType.CSV, separator: str = ',')

Downloads the dataset in local file system :param folder_path: folder in which the file needs to be downloaded :param project_run_entry_id: job run id :param scenario_id: scenario id :param file_type: type of the file :param separator: separator, to be used only in csv case

Returns:

update_sync_options(sync_data_source_id: str, sync_data_source_options: dict)

sync()

download_dataset_schema(folder_path: str, file_name: str = None)

get_dataset_schema()

get_dataset_ontologies_data_types()

saveParquet(filePath: str, limit: int = 1000000)

saveCSV(filePath: str, limit=10000)

get_all_segments(segment_name: str = None) → List[utils.rcclient.entities.segment.Segment]

Get all the created segments of the dataset

Parameters:: segment_name (str, optional) – name of the segment, if given, it fetches only that segment. Defaults to None.
Returns:: List of the segments
Return type:: List[seg_entity.Segment]

add_segment(name: str, description: str, condition: utils.rcclient.entities.segment.ItemExpression | utils.rcclient.entities.segment.GroupExpression | utils.rcclient.entities.segment.GlobalRefExpression | utils.rcclient.entities.segment.DataLabelExpression | utils.rcclient.entities.segment.BooleanExpression, row_limit: int = None) → utils.rcclient.entities.segment.Segment

Add a segment to the dataset

Parameters:

name (str) – name of the segment
description (str) – description of the segment
condition (Union[ seg_entity.ItemExpression, seg_entity.GroupExpression, seg_entity.GlobalRefExpression, seg_entity.DataLabelExpression, seg_entity.BooleanExpression ])
row_limit (int, optional) – Defaults to None.

Returns:

created segment

Return type:

seg_entity.Segment

update_dataset(dataset_meta=None, metadata=None)

utils.rcclient.entities.dataset

Module Contents

Classes

Attributes

`utils.rcclient.entities.dataset`