brk-code

aws-sdk-pandas の API リファレンス

aws-sdk-pandas (aws/aws-sdk-pandas) の公開 API 227 件 —— クラス 61、関数 137、メソッド 29。実際のソースを静的解析して抽出した正確なシグネチャを掲載しています。

リポジトリ: aws/aws-sdk-pandas

種別件数
クラス61
関数137
メソッド29

API 一覧

funcawswrangler._arrow.ensure_df_is_mutable(df:pd.DataFrame) -> pd.DataFrame
Ensure that all columns has the writeable flag True.
funcawswrangler._config.apply_configs(function:FunctionType) -> FunctionType
Decorate some function with configs.
funcawswrangler._data_types.athena2pandas(dtype:str, dtype_backend:str | None=None) -> str
Athena to Pandas data types conversion.
funcawswrangler._data_types.athena2pyarrow(dtype:str, df_type:str | None=None) -> pa.DataType
Athena to PyArrow data types conversion.
funcawswrangler._data_types.athena2quicksight(dtype:str) -> str
Athena to Quicksight data types conversion.
funcawswrangler._data_types.athena2redshift(dtype:str, varchar_length:int=256) -> str
Athena to Redshift data types conversion.
funcawswrangler._data_types.cast_pandas_with_athena_types(df:pd.DataFrame, dtype:dict[str, str], dtype_backend:str | None=None) -> pd.DataFrame
Cast columns in a Pandas DataFrame.
funcawswrangler._data_types.get_arrow_timestamp_unit(data_type:pa.lib.DataType) -> Any
Return unit of pyarrow timestamp.
funcawswrangler._data_types.process_not_inferred_array(ex:pa.ArrowInvalid, values:Any) -> pa.Array
Infer `pyarrow.array` from PyArrow inference exception.
funcawswrangler._data_types.process_not_inferred_dtype(ex:pa.ArrowInvalid) -> pa.DataType
Infer data type from PyArrow inference exception.
funcawswrangler._data_types.pyarrow2athena(dtype:pa.DataType, ignore_null:bool=False) -> str
Pyarrow to Athena data types conversion.
funcawswrangler._data_types.pyarrow2mysql(dtype:pa.DataType, string_type:str) -> str
Pyarrow to MySQL data types conversion.
funcawswrangler._data_types.pyarrow2oracle(dtype:pa.DataType, string_type:str) -> str
Pyarrow to Oracle Database data types conversion.
funcawswrangler._data_types.pyarrow2pandas_extension(dtype:pa.DataType) -> pd.api.extensions.ExtensionDtype | None
Pyarrow to Pandas data types conversion.
funcawswrangler._data_types.pyarrow2postgresql(dtype:pa.DataType, string_type:str) -> str
Pyarrow to PostgreSQL data types conversion.
funcawswrangler._data_types.pyarrow2redshift(dtype:pa.DataType, string_type:str) -> str
Pyarrow to Redshift data types conversion.
funcawswrangler._data_types.pyarrow2sqlserver(dtype:pa.DataType, string_type:str) -> str
Pyarrow to Microsoft SQL Server data types conversion.
funcawswrangler._data_types.pyarrow2timestream(dtype:pa.DataType) -> str
Pyarrow to Amazon Timestream data types conversion.
funcawswrangler._data_types.timestream_type_from_pandas(df:pd.DataFrame) -> list[str]
Extract Amazon Timestream types from a Pandas DataFrame.
classawswrangler._databases.ConnectionAttributes
Connection Attributes.
funcawswrangler._databases.get_connection_attributes(connection:str | None=None, secret_id:str | None=None, catalog_id:str | None=None, dbname:str | None=None, boto3_session:boto3.Session | None=None) -> ConnectionAttributes
Get Connection Attributes.
funcawswrangler._databases.validate_mode(mode:str, allowed_modes:list[str]) -> None
Check if mode is included in allowed_modes.
classawswrangler._distributed.Engine
Execution engine configuration class.
methodawswrangler._distributed.Engine.dispatch_on_engine(func:FunctionType) -> FunctionType
Dispatch on engine function decorator.
methodawswrangler._distributed.Engine.get() -> EngineEnum
Get the configured distribution engine.
methodawswrangler._distributed.Engine.get_installed() -> EngineEnum
Get the installed distribution engine.
methodawswrangler._distributed.Engine.initialize(name:EngineLiteral | None=None) -> None
Initialize the distribution engine.
methodawswrangler._distributed.Engine.is_initialized(name:EngineLiteral | None=None) -> bool
Check if the distribution engine is initialized.
methodawswrangler._distributed.Engine.register(name:EngineLiteral | None=None) -> None
Register the distribution engine dispatch methods.
methodawswrangler._distributed.Engine.set(name:EngineLiteral) -> None
Set the distribution engine.
classawswrangler._distributed.EngineEnum
Execution engine enum.
classawswrangler._distributed.MemoryFormat
Memory format configuration class.
methodawswrangler._distributed.MemoryFormat.get() -> MemoryFormatEnum
Get the configured memory format.
methodawswrangler._distributed.MemoryFormat.get_installed() -> MemoryFormatEnum
Get the installed memory format.
methodawswrangler._distributed.MemoryFormat.set(name:MemoryFormatLiteral) -> None
Set the memory format.
classawswrangler._distributed.MemoryFormatEnum
Memory format enum.
funcawswrangler._utils.block_waiting_available_thread(seq:Sequence[Future], max_workers:int) -> None
Block until any thread became available.
funcawswrangler._utils.boto3_to_primitives(boto3_session:boto3.Session | None=None) -> Boto3PrimitivesType
Convert Boto3 Session to Python primitives.
funcawswrangler._utils.check_duplicated_columns(df:pd.DataFrame) -> Any
Raise an exception if there are duplicated columns names.
funcawswrangler._utils.check_schema_changes(columns_types:dict[str, str], table_input:dict[str, Any] | None, mode:str) -> None
Check schema changes.
funcawswrangler._utils.copy_df_shallow(df:pd.DataFrame) -> pd.DataFrame
Create a shallow copy of the Pandas DataFrame.
funcawswrangler._utils.default_botocore_config() -> botocore.config.Config
Botocore configuration.
funcawswrangler._utils.empty_generator() -> Generator[None, None, None]
Empty Generator.
funcawswrangler._utils.ensure_cpu_count(use_threads:bool | int=True) -> int
Get the number of cpu cores to be used.
funcawswrangler._utils.ensure_session(session:None | boto3.Session=None) -> boto3.Session
Ensure that a valid boto3.Session will be returned.
funcawswrangler._utils.ensure_worker_or_thread_count(use_threads:bool | int=True) -> int
Get the number of CPU cores or Ray workers to be used.
funcawswrangler._utils.get_credentials_from_session(boto3_session:boto3.Session | None=None) -> botocore.credentials.ReadOnlyCredentials
Get AWS credentials from boto3 session.
funcawswrangler._utils.get_directory(path:str) -> str
Extract directory path.
funcawswrangler._utils.get_even_chunks_sizes(total_size:int, chunk_size:int, upper_bound:bool) -> tuple[int, ...]
Calculate even chunks sizes (Best effort).
funcawswrangler._utils.get_region_from_session(boto3_session:boto3.Session | None=None, default_region:str | None=None) -> str
Extract region from session.
funcawswrangler._utils.get_region_from_subnet(subnet_id:str, boto3_session:boto3.Session | None=None) -> str
Extract region from Subnet ID.
funcawswrangler._utils.get_running_futures(seq:Sequence[Future]) -> tuple[Future, ...]
Filter only running futures.
funcawswrangler._utils.import_optional_dependency(name:str) -> ModuleType
Import an optional dependency.
funcawswrangler._utils.is_pandas_frame(obj:Any) -> bool
Check if the passed objected is a Pandas DataFrame.
funcawswrangler._utils.list_sampling(lst:list[Any], sampling:float) -> list[Any]
Random List sampling.
funcawswrangler._utils.parse_path(path:str) -> tuple[str, str]
Split a full S3 path in bucket and key strings.
funcawswrangler._utils.retry(ex:type[Exception], ex_code:str | None=None, base:float=1.0, max_num_tries:int=3) -> Callable[..., Any]
Decorate function with decorrelated Jitter retries.
funcawswrangler._utils.split_pandas_frame(df:pd.DataFrame, splits:int) -> list[pd.DataFrame]
Split a DataFrame into n chunks.
funcawswrangler._utils.table_refs_to_df(tables:list[pa.Table], kwargs:dict[str, Any]) -> pd.DataFrame
Build Pandas DataFrame from list of PyArrow tables.
funcawswrangler._utils.try_it(f:Callable[..., TryItOutputType], ex:Any, *ex_code:str | None=None, *base:float=1.0, *max_num_tries:int=3, *args:Any, **kwargs:Any) -> TryItOutputType
Run function with decorrelated Jitter.
funcawswrangler._utils.wait_any_future_available(seq:Sequence[Future]) -> None
Wait until any future became available.
classawswrangler.annotations.SDKPandasDeprecatedWarning
Deprecated Warning.
classawswrangler.annotations.SDKPandasExperimentalWarning
Experimental Warning.
funcawswrangler.annotations.warn_message(message:str, warning_class:type[Warning], stacklevel:int=2) -> Callable[[FunctionType], FunctionType]
Decorate functions with this to print warnings.
funcawswrangler.athena._executions.get_query_execution(query_execution_id:str, boto3_session:boto3.Session | None=None) -> dict[str, Any]
Fetch query execution details.
funcawswrangler.athena._executions.stop_query_execution(query_execution_id:str, boto3_session:boto3.Session | None=None) -> None
Stop a query execution.
funcawswrangler.athena._executions.wait_query(query_execution_id:str, boto3_session:boto3.Session | None=None, athena_query_wait_polling_delay:float=_QUERY_WAIT_POLLING_DELAY) -> dict[str, Any]
Wait for the query end.
funcawswrangler.athena._read.load_geom_wkt(x)
Load geometry from well-known text.
funcawswrangler.athena._utils.create_athena_bucket(boto3_session:boto3.Session | None=None) -> str
Create the default Athena bucket if it doesn't exist.
funcawswrangler.athena._utils.get_query_columns_types(query_execution_id:str, boto3_session:boto3.Session | None=None) -> dict[str, str]
Get the data type of all columns queried.
funcawswrangler.catalog._delete.delete_column(database:str, table:str, column_name:str, boto3_session:boto3.Session | None=None, catalog_id:str | None=None) -> None
Delete a column in a AWS Glue Catalog table.
funcawswrangler.catalog._delete.delete_database(name:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete a database in AWS Glue Catalog.
funcawswrangler.catalog._delete.delete_table_if_exists(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> bool
Delete Glue table if exists.
funcawswrangler.catalog._get.databases(limit:int=100, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> pd.DataFrame
Get a Pandas DataFrame with all listed databases.
funcawswrangler.catalog._get.get_columns_comments(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> dict[str, str | None]
Get all columns comments.
funcawswrangler.catalog._get.get_columns_parameters(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> dict[str, dict[str, str] | None]
Get all columns parameters.
funcawswrangler.catalog._get.get_connection(name:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> dict[str, Any]
Get Glue connection details.
funcawswrangler.catalog._get.get_databases(catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> Iterator[dict[str, Any]]
Get an iterator of databases.
funcawswrangler.catalog._get.get_table_description(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> str | None
Get table description.
funcawswrangler.catalog._get.get_table_location(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> str
Get table's location on Glue catalog.
funcawswrangler.catalog._get.get_table_number_of_versions(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> int
Get total number of versions.
funcawswrangler.catalog._get.get_table_parameters(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> dict[str, str]
Get all parameters.
funcawswrangler.catalog._get.get_table_versions(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[dict[str, Any]]
Get all versions.
funcawswrangler.catalog._get.table(database:str, table:str, catalog_id:str | None=None, boto3_session:boto3.Session | None=None) -> pd.DataFrame
Get table details as Pandas DataFrame.
funcawswrangler.catalog._utils.does_table_exist(database:str, table:str, boto3_session:boto3.Session | None=None, catalog_id:str | None=None) -> bool
Check if the table exists.
funcawswrangler.catalog._utils.drop_duplicated_columns(df:pd.DataFrame) -> pd.DataFrame
Drop all repeated columns (duplicated names).
funcawswrangler.chime.post_message(webhook:str, message:str) -> Any | None
Send message on an existing Chime Chat rooms.
funcawswrangler.cloudwatch.wait_query(query_id:str, boto3_session:boto3.Session | None=None, cloudwatch_query_wait_polling_delay:float=_QUERY_WAIT_POLLING_DELAY) -> dict[str, Any]
Wait query ends.
classawswrangler.data_api._connector.DataApiConnector
Base class for Data API (RDS, Redshift, etc.) connectors.
methodawswrangler.data_api._connector.DataApiConnector.close() -> None
Close underlying endpoint connections.
classawswrangler.data_api._connector.WaitConfig
Holds standard wait configuration values.
classawswrangler.data_api.rds.RdsDataApi
Provides access to the RDS Data API.
methodawswrangler.data_api.rds.RdsDataApi.begin_transaction(database:str | None=None, schema:str | None=None) -> str
Start an SQL transaction.
methodawswrangler.data_api.rds.RdsDataApi.close() -> None
Close underlying endpoint connections.
methodawswrangler.data_api.rds.RdsDataApi.commit_transaction(transaction_id:str) -> str
Commit an SQL transaction.
methodawswrangler.data_api.rds.RdsDataApi.rollback_transaction(transaction_id:str) -> str
Roll back an SQL transaction.
funcawswrangler.data_api.rds.connect(resource_arn:str, database:str, secret_arn:str='', boto3_session:boto3.Session | None=None, **kwargs:Any) -> RdsDataApi
Create a RDS Data API connection.
classawswrangler.data_api.redshift.RedshiftDataApi
Provides access to a Redshift cluster via the Data API.
methodawswrangler.data_api.redshift.RedshiftDataApi.begin_transaction(database:str | None=None, schema:str | None=None) -> str
Start an SQL transaction.
methodawswrangler.data_api.redshift.RedshiftDataApi.close() -> None
Close underlying endpoint connections.
methodawswrangler.data_api.redshift.RedshiftDataApi.commit_transaction(transaction_id:str) -> str
Commit an SQL transaction.
methodawswrangler.data_api.redshift.RedshiftDataApi.rollback_transaction(transaction_id:str) -> str
Roll back an SQL transaction.
funcawswrangler.data_quality._get.get_ruleset(name:str | list[str], boto3_session:boto3.Session | None=None) -> pd.DataFrame
Get a Data Quality ruleset.
classawswrangler.distributed.ray._core.RayLogger
Create discrete Logger instance for Ray Tasks.
methodawswrangler.distributed.ray._core.RayLogger.get_logger(name:str | Any=None) -> logging.Logger | None
Return logger object.
funcawswrangler.distributed.ray._core.ray_get(futures:'ray.ObjectRef[Any]' | list['ray.ObjectRef[Any]']) -> Any
Run ray.get on futures if distributed.
funcawswrangler.distributed.ray._core.ray_logger(function:FunctionType, configure_logging:bool=True, logging_level:int=logging.INFO) -> FunctionType
Decorate callable to add RayLogger.
funcawswrangler.distributed.ray._core.ray_remote(**options:Any) -> Callable[[FunctionType], FunctionType]
Decorate with @ray.remote providing .options().
funcawswrangler.distributed.ray._core.remote_decorator(function:FunctionType) -> FunctionType
Decorate callable to wrap within ray.remote.
funcawswrangler.distributed.ray._register.register_ray() -> None
Register dispatched Ray and Modin (on Ray) methods.
classawswrangler.distributed.ray.datasources.arrow_csv_datasink.ArrowCSVDatasink
A datasink that writes CSV files using Arrow.
methodawswrangler.distributed.ray.datasources.arrow_csv_datasink.ArrowCSVDatasink.write_block(file:io.TextIOWrapper, block:BlockAccessor) -> None
Write a block of data to a file.
classawswrangler.distributed.ray.datasources.arrow_orc_datasink.ArrowORCDatasink
A datasink that writes CSV files using Arrow.
methodawswrangler.distributed.ray.datasources.arrow_orc_datasink.ArrowORCDatasink.write_block(file:io.TextIOWrapper, block:BlockAccessor) -> None
Write a block of data to a file.
classawswrangler.distributed.ray.datasources.arrow_parquet_datasink.ArrowParquetDatasink
A datasink that writes Parquet files.
methodawswrangler.distributed.ray.datasources.arrow_parquet_datasink.ArrowParquetDatasink.write_block(file:pa.NativeFile, block:BlockAccessor) -> None
Write a block of data to a file.
funcawswrangler.distributed.ray.modin._core.modin_repartition(function:FunctionType) -> FunctionType
Decorate callable to repartition Modin data frame.
funcawswrangler.dynamodb._delete.delete_items(items:list[dict[str, Any]], table_name:str, boto3_session:boto3.Session | None=None) -> None
Delete all items in the specified DynamoDB table.
funcawswrangler.dynamodb._utils.get_table(table_name:str, boto3_session:boto3.Session | None=None) -> 'Table'
Get DynamoDB table object for specified table name.
funcawswrangler.dynamodb._write.put_df(df:pd.DataFrame, table_name:str, boto3_session:boto3.Session | None=None, use_threads:bool | int=True) -> None
Write all items from a DataFrame to a DynamoDB.
funcawswrangler.dynamodb._write.put_json(path:str | Path, table_name:str, boto3_session:boto3.Session | None=None, use_threads:bool | int=True) -> None
Write all items from JSON file to a DynamoDB.
funcawswrangler.emr.get_cluster_state(cluster_id:str, boto3_session:boto3.Session | None=None) -> str
Get the EMR cluster state.
funcawswrangler.emr.get_step_state(cluster_id:str, step_id:str, boto3_session:boto3.Session | None=None) -> str
Get EMR step state.
funcawswrangler.emr.submit_ecr_credentials_refresh(cluster_id:str, path:str, action_on_failure:_ActionOnFailureLiteral='CONTINUE', boto3_session:boto3.Session | None=None) -> str
Update internal ECR credentials.
funcawswrangler.emr.submit_step(cluster_id:str, command:str, name:str='my-step', action_on_failure:_ActionOnFailureLiteral='CONTINUE', script:bool=False, boto3_session:boto3.Session | None=None) -> str
Submit new job in the EMR Cluster.
funcawswrangler.emr.submit_steps(cluster_id:str, steps:list[dict[str, Any]], boto3_session:boto3.Session | None=None) -> list[str]
Submit a list of steps.
funcawswrangler.emr.terminate_cluster(cluster_id:str, boto3_session:boto3.Session | None=None) -> None
Terminate EMR cluster.
classawswrangler.emr_serverless.HiveRunJobArgs
Typed dictionary defining the Hive job run arguments.
classawswrangler.exceptions.AlreadyExists
AlreadyExists.
classawswrangler.exceptions.CalculationFailed
CalculationFailed exception.
classawswrangler.exceptions.EMRServerlessJobError
EMRServerlessJobError.
classawswrangler.exceptions.EmptyDataFrame
EmptyDataFrame exception.
classawswrangler.exceptions.FailedQualityCheck
FailedQualityCheck.
classawswrangler.exceptions.InvalidArgument
Invalid argument.
classawswrangler.exceptions.InvalidArgumentCombination
Invalid argument combination.
classawswrangler.exceptions.InvalidArgumentType
Invalid argument type.
classawswrangler.exceptions.InvalidArgumentValue
Invalid argument value.
classawswrangler.exceptions.InvalidCompression
Invalid compression format.
classawswrangler.exceptions.InvalidConfiguration
InvalidConfiguration exception.
classawswrangler.exceptions.InvalidConnection
InvalidConnection exception.
classawswrangler.exceptions.InvalidCtasApproachQuery
InvalidCtasApproachQuery exception.
classawswrangler.exceptions.InvalidDataFrame
InvalidDataFrame.
classawswrangler.exceptions.InvalidDatabaseType
InvalidDatabaseEngine exception.
classawswrangler.exceptions.InvalidFile
InvalidFile.
classawswrangler.exceptions.InvalidRedshiftDistkey
InvalidRedshiftDistkey exception.
classawswrangler.exceptions.InvalidRedshiftDiststyle
InvalidRedshiftDiststyle exception.
classawswrangler.exceptions.InvalidRedshiftPrimaryKeys
InvalidRedshiftPrimaryKeys exception.
classawswrangler.exceptions.InvalidRedshiftSortkey
InvalidRedshiftSortkey exception.
classawswrangler.exceptions.InvalidRedshiftSortstyle
InvalidRedshiftSortstyle exception.
classawswrangler.exceptions.InvalidRulesetDefinition
InvalidRulesetDefinition.
classawswrangler.exceptions.InvalidSchemaConvergence
InvalidSchemaMerge exception.
classawswrangler.exceptions.InvalidTable
InvalidTable exception.
classawswrangler.exceptions.NeptuneLoadError
NeptuneLoadError.
classawswrangler.exceptions.NoFilesFound
NoFilesFound exception.
classawswrangler.exceptions.NotSupported
NotSupported.
classawswrangler.exceptions.PolicyResourceConflict
PolicyResourceConflict.
classawswrangler.exceptions.QueryCancelled
QueryCancelled exception.
classawswrangler.exceptions.QueryFailed
QueryFailed exception.
classawswrangler.exceptions.RedshiftLoadError
RedshiftLoadError exception.
classawswrangler.exceptions.ResourceDoesNotExist
ResourceDoesNotExist.
classawswrangler.exceptions.S3SelectRequestIncomplete
S3SelectRequestIncomplete.
classawswrangler.exceptions.ServiceApiError
ServiceApiError exception.
classawswrangler.exceptions.SessionFailed
SessionFailed exception.
classawswrangler.exceptions.TimestreamLoadError
TimestreamLoadError exception.
classawswrangler.exceptions.UndetectedType
UndetectedType exception.
classawswrangler.exceptions.UnsupportedType
UnsupportedType exception.
classawswrangler.neptune._client.NeptuneClient
Class representing a Neptune cluster connection.
methodawswrangler.neptune._client.NeptuneClient.load_status(load_id:str) -> Any
Return the status of the load job to the Neptune cluster.
methodawswrangler.neptune._client.NeptuneClient.read_opencypher(query:str, headers:Any=None) -> Any
Execute the provided openCypher query.
methodawswrangler.neptune._client.NeptuneClient.read_sparql(query:str, headers:Any=None) -> Any
Execute the given query and returns the results.
methodawswrangler.neptune._client.NeptuneClient.status() -> Any
Return the status of the Neptune cluster.
methodawswrangler.neptune._client.NeptuneClient.write_gremlin(query:str) -> bool
Execute a Gremlin write query.
methodawswrangler.neptune._client.NeptuneClient.write_sparql(query:str, headers:Any=None) -> bool
Execute the specified SPARQL write statements.
funcawswrangler.neptune._neptune.connect(host:str, port:int, iam_enabled:bool=False, **kwargs:Any) -> NeptuneClient
Create a connection to a Neptune cluster.
funcawswrangler.neptune._neptune.execute_gremlin(client:NeptuneClient, query:str) -> pd.DataFrame
Return results of a Gremlin traversal as pandas DataFrame.
funcawswrangler.neptune._neptune.execute_sparql(client:NeptuneClient, query:str) -> pd.DataFrame
Return results of a SPARQL query as pandas DataFrame.
classawswrangler.neptune._utils.WriteDFType
DataFrame type enum.
funcawswrangler.neptune._utils.write_gremlin_df(client:'NeptuneClient', df:pd.DataFrame, mode:WriteDFType, batch_size:int) -> bool
Write the provided DataFrame using Gremlin.
funcawswrangler.opensearch._write.create_index(client:'opensearchpy.OpenSearch', index:str, doc_type:str | None=None, settings:dict[str, Any] | None=None, mappings:dict[str, Any] | None=None) -> dict[str, Any]
Create an index.
funcawswrangler.opensearch._write.delete_index(client:'opensearchpy.OpenSearch', index:str) -> dict[str, Any]
Delete an index.
funcawswrangler.quicksight._delete.delete_all_dashboards(account_id:str | None=None, regex_filter:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete all dashboards.
funcawswrangler.quicksight._delete.delete_all_data_sources(account_id:str | None=None, regex_filter:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete all data sources.
funcawswrangler.quicksight._delete.delete_all_datasets(account_id:str | None=None, regex_filter:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete all datasets.
funcawswrangler.quicksight._delete.delete_all_templates(account_id:str | None=None, regex_filter:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete all templates.
funcawswrangler.quicksight._delete.delete_dashboard(name:str | None=None, dashboard_id:str | None=None, version_number:int | None=None, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete a dashboard.
funcawswrangler.quicksight._delete.delete_data_source(name:str | None=None, data_source_id:str | None=None, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete a data source.
funcawswrangler.quicksight._delete.delete_dataset(name:str | None=None, dataset_id:str | None=None, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete a dataset.
funcawswrangler.quicksight._delete.delete_template(name:str | None=None, template_id:str | None=None, version_number:int | None=None, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> None
Delete a template.
funcawswrangler.quicksight._get_list.get_dashboard_ids(name:str, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[str]
Get QuickSight dashboard IDs given a name.
funcawswrangler.quicksight._get_list.get_data_source_arns(name:str, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[str]
Get QuickSight Data source ARNs given a name.
funcawswrangler.quicksight._get_list.get_data_source_ids(name:str, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[str]
Get QuickSight data source IDs given a name.
funcawswrangler.quicksight._get_list.get_dataset_ids(name:str, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[str]
Get QuickSight dataset IDs given a name.
funcawswrangler.quicksight._get_list.get_template_ids(name:str, account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[str]
Get QuickSight template IDs given a name.
funcawswrangler.quicksight._get_list.list_dashboards(account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[dict[str, Any]]
List dashboards in an AWS account.
funcawswrangler.quicksight._get_list.list_data_sources(account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[dict[str, Any]]
List all QuickSight Data sources summaries.
funcawswrangler.quicksight._get_list.list_datasets(account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[dict[str, Any]]
List all QuickSight datasets summaries.
funcawswrangler.quicksight._get_list.list_groups(namespace:str='default', account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[dict[str, Any]]
List all QuickSight Groups.
funcawswrangler.quicksight._get_list.list_templates(account_id:str | None=None, boto3_session:boto3.Session | None=None) -> list[dict[str, Any]]
List all QuickSight templates.
funcawswrangler.s3._describe.get_bucket_region(bucket:str, boto3_session:boto3.Session | None=None) -> str
Get bucket region name.
funcawswrangler.s3._fs.get_botocore_valid_kwargs(function_name:str, s3_additional_kwargs:dict[str, Any]) -> dict[str, Any]
Filter and keep only the valid botocore key arguments.
funcawswrangler.s3._list.does_object_exist(path:str, s3_additional_kwargs:dict[str, Any] | None=None, boto3_session:boto3.Session | None=None, version_id:str | None=None) -> bool
Check if object exists on S3.
funcawswrangler.s3._list.list_buckets(boto3_session:boto3.Session | None=None) -> list[str]
List Amazon S3 buckets.
funcawswrangler.s3._s3_tables_mgmt.create_namespace(table_bucket_arn:str, namespace:str, boto3_session:boto3.Session | None=None) -> str
Create a namespace in an S3 Table Bucket.
funcawswrangler.s3._s3_tables_mgmt.create_table_bucket(name:str, boto3_session:boto3.Session | None=None) -> str
Create an S3 Table Bucket.
funcawswrangler.s3._s3_tables_mgmt.delete_namespace(table_bucket_arn:str, namespace:str, boto3_session:boto3.Session | None=None) -> None
Delete a namespace from an S3 Table Bucket.
funcawswrangler.s3._s3_tables_mgmt.delete_table_bucket(table_bucket_arn:str, boto3_session:boto3.Session | None=None) -> None
Delete an S3 Table Bucket.
funcawswrangler.s3._vectors._mgmt.delete_vector_bucket(name:str | None=None, *arn:str | None=None, *boto3_session:boto3.Session | None=None) -> None
Delete an Amazon S3 Vectors bucket.
funcawswrangler.s3._vectors._mgmt.delete_vector_index(*name:str | None=None, *arn:str | None=None, *vector_bucket:str | None=None, *vector_bucket_arn:str | None=None, *boto3_session:boto3.Session | None=None) -> None
Delete a vector index.
funcawswrangler.s3._vectors._mgmt.get_vector_bucket(name:str | None=None, *arn:str | None=None, *boto3_session:boto3.Session | None=None) -> dict[str, Any]
Get attributes of a vector bucket.
funcawswrangler.s3._wait.wait_objects_exist(paths:list[str], delay:float | None=None, max_attempts:int | None=None, use_threads:bool | int=True, boto3_session:boto3.Session | None=None) -> None
Wait Amazon S3 objects exist.
funcawswrangler.s3._wait.wait_objects_not_exist(paths:list[str], delay:float | None=None, max_attempts:int | None=None, use_threads:bool | int=True, boto3_session:boto3.Session | None=None) -> None
Wait Amazon S3 objects not exist.
funcawswrangler.s3._write_excel.to_excel(df:pd.DataFrame, path:str, boto3_session:boto3.Session | None=None, s3_additional_kwargs:dict[str, Any] | None=None, use_threads:bool | int=True, **pandas_kwargs:Any) -> str
Write EXCEL file on Amazon S3.
funcawswrangler.secretsmanager.get_secret(name:str, boto3_session:boto3.Session | None=None) -> str | bytes
Get secret value.
funcawswrangler.secretsmanager.get_secret_json(name:str, boto3_session:boto3.Session | None=None) -> dict[str, Any]
Get JSON secret value.
funcawswrangler.sts.get_account_id(boto3_session:boto3.Session | None=None) -> str
Get Account ID.
funcawswrangler.sts.get_current_identity_arn(boto3_session:boto3.Session | None=None) -> str
Get current user/role ARN.
funcawswrangler.sts.get_current_identity_name(boto3_session:boto3.Session | None=None) -> str
Get current user/role name.
funcawswrangler.timestream._create.create_database(database:str, kms_key_id:str | None=None, tags:dict[str, str] | None=None, boto3_session:boto3.Session | None=None) -> str
Create a new Timestream database.
funcawswrangler.timestream._delete.delete_database(database:str, boto3_session:boto3.Session | None=None) -> None
Delete a given Timestream database.
funcawswrangler.timestream._delete.delete_table(database:str, table:str, boto3_session:boto3.Session | None=None) -> None
Delete a given Timestream table.
funcawswrangler.timestream._list.list_databases(boto3_session:boto3.Session | None=None) -> list[str]
List all databases in timestream.
funcawswrangler.timestream._list.list_tables(database:str | None=None, boto3_session:boto3.Session | None=None) -> list[str]
List tables in timestream.
classawswrangler.typing.ArrowDecryptionConfiguration
Configuration for Arrow file decrypting.
classawswrangler.typing.ArrowEncryptionConfiguration
Configuration for Arrow file encrypting.
classawswrangler.typing.AthenaUNLOADSettings
Typed dictionary defining the settings for using UNLOAD.
classawswrangler.typing.GlueTableSettings
Typed dictionary defining the settings for the Glue table.
classawswrangler.typing.TimestreamBatchLoadReportS3Configuration
Report configuration for a batch load task.

この情報について

掲載しているシグネチャは aws/aws-sdk-pandas の公開ソースコードを Python の ast モジュールで静的解析し、引数名・デフォルト値・ 型注釈・戻り値型をそのまま抽出したものです。実装コードは保存していません。 詳しくは仕組みの解説をご覧ください。

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