Python tutorial for AWS Cloud9

This tutorial shows you how to run Python code in an AWS Cloud9 development environment.

Following this tutorial might result in charges to your AWS account. These include possible charges for services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3). For more information, see Amazon EC2 Pricing and Amazon S3 Pricing.

Prerequisites

Before you use this tutorial, be sure to meet the following requirements.

• You have an AWS Cloud9 EC2 development environment

  This tutorial assumes that you have an EC2 environment, and that the environment is connected to an Amazon EC2 instance running Amazon Linux or Ubuntu Server. See Creating an EC2 Environment for details.

  If you have a different type of environment or operating system, you might need to adapt this tutorial's instructions.

• You have opened the AWS Cloud9 IDE for that environment

  When you open an environment, AWS Cloud9 opens the IDE for that environment in your web browser. See Opening an environment in AWS Cloud9 for details.

Step 1: Install Python

1. In a terminal session in the AWS Cloud9 IDE, confirm whether Python is already installed by running the python --version command. (To start a new terminal session, on the menu bar choose Window, New Terminal.) If Python is installed, skip ahead to Step 2: Add code.

2. Run the yum update (for Amazon Linux) or apt update (for Ubuntu Server) command to help ensure the latest security updates and bug fixes are installed.

   For Amazon Linux:

   sudo yum -y update

   For Ubuntu Server:

   sudo apt update

3. Install Python by running the install command.

   For Amazon Linux:

   sudo yum -y install python3

   For Ubuntu Server:

   sudo apt-get install python3

Step 2: Add code

In the AWS Cloud9 IDE, create a file with the following content and save the file with the name hello.py. (To create a file, on the menu bar choose File, New File. To save the file, choose File, Save.)

import sys

print('Hello, World!')

print('The sum of 2 and 3 is 5.')

sum = int(sys.argv[1]) + int(sys.argv[2])

print('The sum of {0} and {1} is {2}.'.format(sys.argv[1], sys.argv[2], sum))

Step 3: Run the code

1. In the AWS Cloud9 IDE, on the menu bar choose Run, Run Configurations, New Run Configuration.

2. On the [New] - Stopped tab, enter hello.py 5 9 for Command. In the code, 5 represents sys.argv[1], and 9 represents sys.argv[2].

3. Choose Run and compare your output.

   Hello, World!
   The sum of 2 and 3 is 5.
   The sum of 5 and 9 is 14.

4. By default, AWS Cloud9 automatically selects a runner for your code. To change the runner, choose Runner, and then choose Python 2 or Python 3.

   Note

   You can create custom runners for specific versions of Python. For details, see Create a Builder or Runner.

Step 4: Install and configure the AWS SDK for Python (Boto3)

The AWS SDK for Python (Boto3) enables you to use Python code to interact with AWS services like Amazon S3. For example, you can use the SDK to create an Amazon S3 bucket, list your available buckets, and then delete the bucket you just created.

Install pip

In the AWS Cloud9 IDE, confirm whether pip is already installed for the active version of Python by running the python -m pip --version command. If pip is installed, skip to the next section.

To install pip, run the following commands. Because sudo is in a different environment from your user, you must specify the version of Python to use if it differs from the current aliased version.

curl -O https://bootstrap.pypa.io/get-pip.py # Get the install script.
sudo python3 get-pip.py                     # Install pip for Python 3.
python -m pip --version                      # Verify pip is installed.
rm get-pip.py                                # Delete the install script.

For more information, see Installation on the pip website.

Install the AWS SDK for Python (Boto3)

After you install pip, install the AWS SDK for Python (Boto3) by running the pip install command.

sudo python3 -m pip install boto3  # Install boto3 for Python 3.
python -m pip show boto3            # Verify boto3 is installed for the current version of Python.

For more information, see the "Installation" section of Quickstart in the AWS SDK for Python (Boto3).

Set up credentials in your environment

Each time you use the AWS SDK for Python (Boto3) to call an AWS service, you must provide a set of credentials with the call. These credentials determine whether the SDK has the necessary permissions to make the call. If the credentials don't cover the necessary permissions, the call fails.

To store your credentials within the environment, follow the instructions in Calling AWS services from an environment in AWS Cloud9, and then return to this topic.

For additional information, see Credentials in the AWS SDK for Python (Boto3).

Step 5: Add AWS SDK code

Add code that uses Amazon S3 to create a bucket, list your available buckets, and optionally delete the bucket you just created.

In the AWS Cloud9 IDE, create a file with the following content and save the file with the name s3.py.

import sys
import boto3
from botocore.exceptions import ClientError


def list_my_buckets(s3_resource):
    print("Buckets:\n\t", *[b.name for b in s3_resource.buckets.all()], sep="\n\t")


def create_and_delete_my_bucket(s3_resource, bucket_name, keep_bucket):
    list_my_buckets(s3_resource)

    try:
        print("\nCreating new bucket:", bucket_name)
        bucket = s3_resource.create_bucket(
            Bucket=bucket_name,
            CreateBucketConfiguration={
                "LocationConstraint": s3_resource.meta.client.meta.region_name
            },
        )
    except ClientError as e:
        print(
            f"Couldn't create a bucket for the demo. Here's why: "
            f"{e.response['Error']['Message']}"
        )
        raise

    bucket.wait_until_exists()
    list_my_buckets(s3_resource)

    if not keep_bucket:
        print("\nDeleting bucket:", bucket.name)
        bucket.delete()

        bucket.wait_until_not_exists()
        list_my_buckets(s3_resource)
    else:
        print("\nKeeping bucket:", bucket.name)


def main():
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument("bucket_name", help="The name of the bucket to create.")
    parser.add_argument("region", help="The region in which to create your bucket.")
    parser.add_argument(
        "--keep_bucket",
        help="Keeps the created bucket. When not "
        "specified, the bucket is deleted "
        "at the end of the demo.",
        action="store_true",
    )

    args = parser.parse_args()
    s3_resource = (
        boto3.resource("s3", region_name=args.region)
        if args.region
        else boto3.resource("s3")
    )
    try:
        create_and_delete_my_bucket(s3_resource, args.bucket_name, args.keep_bucket)
    except ClientError:
        print("Exiting the demo.")


if __name__ == "__main__":
    main()

Step 6: Run the AWS SDK code

1. On the menu bar, choose Run, Run Configurations, New Run Configuration.

2. For Command, enter s3.py my-test-bucket us-west-2, where my-test-bucket is the name of the bucket to create, and us-west-2 is the ID of the AWS Region where your bucket is created. By default, your bucket is deleted before the script exits. To keep your bucket, add --keep_bucket to your command. For a list of AWS Region IDs, see Amazon Simple Storage Service Endpoints and Quotas in the AWS General Reference.

   Note

   Amazon S3 bucket names must be unique across AWS—not just your AWS account.

3. Choose Run, and compare your output.

   
   Buckets:
   
           a-pre-existing-bucket
   
   Creating new bucket: my-test-bucket
   Buckets:
   
           a-pre-existing-bucket
           my-test-bucket
   
   Deleting bucket: my-test-bucket
   Buckets:
   
           a-pre-existing-bucket
               

Step 7: Clean up

To prevent ongoing charges to your AWS account after you're done with this tutorial, delete the AWS Cloud9 environment. For instructions, see Deleting an environment in AWS Cloud9.