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DataPipeline module#

Index > DataPipeline

Auto-generated documentation for DataPipeline type annotations stubs module mypy-boto3-datapipeline.

How to install#

VSCode extension#

Add AWS Boto3 extension to your VSCode and run AWS boto3: Quick Start command.

Click Modify and select boto3 common and DataPipeline.

From PyPI with pip#

Install boto3-stubs for DataPipeline service.

# install with boto3 type annotations
python -m pip install 'boto3-stubs[datapipeline]'


# Lite version does not provide session.client/resource overloads
# it is more RAM-friendly, but requires explicit type annotations
python -m pip install 'boto3-stubs-lite[datapipeline]'


# standalone installation
python -m pip install mypy-boto3-datapipeline

How to uninstall#

python -m pip uninstall -y mypy-boto3-datapipeline

Usage#

Code samples can be found in Examples.

DataPipelineClient#

Type annotations and code completion for boto3.client("datapipeline") as DataPipelineClient boto3 documentation

# DataPipelineClient usage example

from boto3.session import Session

from mypy_boto3_datapipeline.client import DataPipelineClient

def get_client() -> DataPipelineClient:
    return Session().client("datapipeline")

Paginators#

Type annotations and code completion for paginators from boto3.client("datapipeline").get_paginator("...").

# DescribeObjectsPaginator usage example

from boto3.session import Session

from mypy_boto3_datapipeline.paginator import DescribeObjectsPaginator

def get_describe_objects_paginator() -> DescribeObjectsPaginator:
    return Session().client("datapipeline").get_paginator("describe_objects"))

Literals#

Type annotations for literals used in methods and schemas.

# DescribeObjectsPaginatorName usage example

from mypy_boto3_datapipeline.literals import DescribeObjectsPaginatorName

def get_value() -> DescribeObjectsPaginatorName:
    return "describe_objects"

Typed dictionaries#

Type annotations for typed dictionaries used in methods and schema.

# ParameterValueTypeDef usage example

from mypy_boto3_datapipeline.type_defs import ParameterValueTypeDef

def get_value() -> ParameterValueTypeDef:
    return {
        "id": ...,
        "stringValue": ...,
    }