AWS, Boto3

How to Specify AWS Region For Boto3 Library and Resolve Boto3 NoRegionError?

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by Vikram Aruchamy

AWS Boto3 is a Python library that makes it easy to interact with AWS services. When you use Boto3, you need to specify the region that you want to connect to. This is because AWS services are hosted in different regions around the world.

Basic Example

The following example shows how to specify region while creating a Boto3 client.

s3_client = boto3.client('s3', region_name=region)

This tutorial teaches what region names, region aliases, and different methods to set region names globally and how to specify a region applicable to a specific program instance.

Understanding Region Names, Region Aliases and Mapping

A region name is a unique identifier for an AWS region. It is used to identify the geographic location of an AWS region.

Region aliases are alternative names for AWS regions. For example, the region alias “us-east-1” is an alias for the region named “US East (N. Virginia)“.

You can specify region names when you create or manage AWS resources using Boto3. For example, you could create an Amazon S3 bucket in the us-east-1 region by specifying the region name us-east-1. If you are unsure which region name to use, you can specify the region alias. The region alias will always resolve to the correct region name.

Here are some examples of region aliases:






To know more about the available region and their aliases, refer to the Regions Docs.

Specify Region For Boto3 Client Using the Region_name Argument

The Boto3 client is a low-level service client that represents the AWS services.

To specify the region for the Boto3 client, use the region_name argument in the boto3.client() constructor method and pass the desired region alias as the value.


The following code demonstrates creating a client for the S3 service at the region us-west-1.

import boto3

region = 'us-west-1'

s3_client = boto3.client('s3', region_name=region)




Specify Region For Boto3 Resource Using the Region_name Argument

The Boto3 Resource represents an object-oriented interface to AWS services.

To specify the region for the Boto3 resource, use the region_name argument in the boto3.resource( ) constructor method and pass the desired region alias as the value.


The following code demonstrates how to create a resource representation for the S3 service at the region us-west-1.

import boto3

region = 'us-west-1'

s3_resource = boto3.resource('s3', region_name=region)




Specify Region For Boto3 Client Using the Lambda Function Region Property

In AWS Lambda, specifying the region for Boto3 clients is essential for ensuring your serverless applications interact with AWS services in the correct geographic area.

To achieve this, you can utilize the Lambda function’s region property, which dynamically extracts the region in which the Lambda function is deployed.

This method helps avoid hardcoding region names and makes your Lambda functions region-aware. By accessing the region from the context object and incorporating it into Boto3 client creation, you can seamlessly work with AWS services in the same region as your Lambda function.


import os
import boto3

def main(event, context):
    lambda_region = os.environ['AWS_REGION']

    print(f'Lambda function region is: {lambda_region}')

    s3_client = boto3.client('s3', region_name=lambda_region)


    # Desired code of your lambda function

This approach ensures portability and compliance with regional requirements, enhancing the reliability of your serverless applications in AWS Lambda.

Configuring Default Region For Boto3 Client Using Various Methods(Shell, Environment Variables, Python)

This section explains configuring the default region for the AWS CLI applications.

Configuring Using Command line

aws configure

Setting Environment variable in shell

export AWS_DEFAULT_REGION=us-east-1

Setting Environment Variable in Docker Container


Setting Region in Python Program

If you create more than one Boto3 client to interact with various services, you can set the region globally during the start of your program.

However, remember this setting will persist ONLY until the end of the current execution, and it will not affect the other Boto3 interactions.


import os
import boto3

os.environ['AWS_DEFAULT_REGION'] = 'us-west-1'

s3_client = boto3.client('s3')


Exception Handling for NoRegionError in Boto3

The boto3 client NoRegionError: You must specify a region error exception is raised by Boto3 when a client is created without specifying a region. This can happen if the region_name argument is not passed to the client() method or if the AWS_DEFAULT_REGION environment variable is not set.

To handle the NoRegionError exception, you can use the try and except blocks in Python and handle the NoRegionError gracefully.


import boto3

from botocore.exceptions import NoRegionError


    s3_client = boto3.client('s3')

    response = s3_client.list_buckets()


except NoRegionError as e:

    print("NoRegionError: Please specify a region when creating the AWS client.")

except Exception as e:

    print(f"An error occurred: {str(e)}")

Benefits of Specifying a Region for a Boto3

Improved performance – When you specify the region, Boto3 can optimize its requests to the AWS API. This can result in improved performance, especially for operations that involve a lot of data transfer.

Increased reliability – Specifying the region can help avoid errors when you connect to the wrong region. For example, if you try to create an S3 bucket in the wrong region, you will receive an error.

Reduced costs – Some AWS services charge different rates for different regions. By specifying the region, you can ensure that you are only charged the rates for the region that you are using.


Specifying the region for a Boto3 client is a best practice that can help improve performance, reliability, and cost-effectiveness. This tutorial has explained how to set regions appropriately and how to handle the exception that occurs when the region is not set properly.

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