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How to Upgrade Python Version from Cloud Shell AWS?

By: ninad magdum | July 24, 2023 | 24 mins read

AWS Cloud Shell is a robust web-based Integrated Development Environment (IDE) supplied by Amazon Web Services (AWS). So, we are going to learn more about how to upgrade the Python version from the cloud shell AWS. Cloud Shell provides a secure, browser-based command line interface (CLI) for controlling and communicating with your AWS services.

In this blog article, we will look at the advantages of AWS Cloud Shell and walk you through installing Python. Additionally, it will demonstrate how to upgrade the Python version from the cloud shell AWS for increased development possibilities.

Upgrading Python Version from AWS Cloud Shell and Its Advantages

AWS Cloud Shell offers several benefits that make it an indispensable tool in your AWS workflow:

Convenience

With Cloud Shell AWS, you can use your browser to access a pre-configured AWS environment. So, removing the need for local installs and customizations. The virtual is hosted on AWS hardware and has built-in security mechanisms to ensure a safe and isolated development environment.

Persistent Storage

Your data and files are automatically saved, allowing you to smoothly resume your work between sessions.

Pre-installed Tools

Cloud Shell includes pre-installed AWS CLI tools and SDKs, allowing you to connect with AWS services quickly. Cloud Shell connects smoothly with other AWS services, allowing smooth operations and eliminating context switching.

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How to upgrade Python version from Cloud Shell AWS?

The Cloud Shell is an important resource that we can use the Python programming language to satisfy our requirements.

Python, when combined with AWS Cloud Shell, opens a world of possibilities for building, managing, and deploying applications in the cloud. AWS Cloud Shell is an interactive command-line interface (CLI) that allows developers to access a pre-configured environment straight from their web browser.

Here are some key details about using Python with AWS Cloud Shell:

Seamless Integration

AWS Cloud Shell virtual environment comes pre-installed with Python, making it a natural choice for Python developers. You can quickly start working with Python without the need for any additional setup or installation.

Also, in continuation of this, we must be aware of how to upgrade the Python version from the cloud shell AWS.

AWS SDKs and Boto3

Python has excellent support for AWS services through the AWS SDK for Python (Boto3) is the official SDK for Python. Boto3 provides a simple and intuitive way to interact with various AWS services, such as Amazon S3, EC2, Lambda, DynamoDB, and more. With the AWS Cloud Shell command-line interface, you have Boto3 readily available. Thus allowing you to automate AWS resource management and build powerful cloud-based applications using Python.

Infrastructure as Code

Python’s versatility and readability make it an ideal choice for Infrastructure as Code (IaC) deployments. AWS CloudFormation, AWS’s IAC service, allows you to define your infrastructure using YAML or JSON templates. Python can be used to generate and manage these templates dynamically, making it easier to automate infrastructure provisioning and orchestration.

Serverless Computing

AWS Lambda, the serverless compute service by AWS, supports Python as one of its primary runtime environments. Python’s simplicity and extensive library ecosystem make it well-suited for writing Lambda functions. You can use AWS Cloud Shell to develop, test, and deploy Python-based serverless applications, leveraging the benefits of Lambda’s automatic scaling and pay-per-use pricing model.

Data Processing and Analysis

Python is widely used for data processing and analysis tasks. AWS Cloud Shell provides seamless integration with AWS Glue, Amazon Athena, Amazon EMR (Elastic MapReduce), and other data processing services. You can write Python scripts to extract, transform, and analyze data using libraries such as Pandas, NumPy, and Matplotlib, and leverage AWS Cloud Shell’s resources to run these scripts efficiently.

DevOps and Automation

Python’s extensive ecosystem of libraries and frameworks makes it a popular choice for DevOps tasks and automation. With AWS Cloud Shell, you can use Python to build and manage CI/CD pipelines, perform infrastructure testing, and automate deployment workflows. Python frameworks like Flask and Django can also be used to build web-based dashboards and management interfaces for your AWS resources.

Training and Education

AWS Cloud Shell, combined with Python, offers an excellent platform for learning and training in the AWS ecosystem. Aspiring developers and students can use Python to experiment with various AWS services and APIs, gaining hands-on experience in cloud computing. The ability to access AWS resources directly from the Cloud Shell environment makes it a convenient tool for teaching and demonstrating AWS concepts.

Overall, Python’s versatility, combined with the seamless integration provided by AWS Cloud Shell, opens a world of possibilities for developing, managing, and deploying applications on the AWS cloud platform. Whether it is building serverless functions, automating infrastructure management, processing data, or exploring AWS services, Python and AWS Cloud Shell make a powerful combination for cloud-based development.

Now, let us learn more about some use cases where AWS Cloud Shell was used and Python was incorporated.

Use Case 1: Serverless Application Development with AWS Cloud Shell and Upgrading Python Version

Serverless architecture has gained popularity for its scalability, cost efficiency, and reduced operational overhead. AWS provides a comprehensive suite of serverless services, such as AWS Lambda, Amazon API Gateway, and AWS Step Functions, that allow developers to build and deploy serverless applications easily. AWS Cloud Shell can significantly enhance the development experience for serverless applications by providing a pre-configured environment and convenient access to AWS resources.

Scenario

Suppose you are a developer working on a serverless application that requires multiple AWS services, such as AWS Lambda functions, Amazon S3 buckets, and DynamoDB tables. You want a streamlined and efficient development workflow without the hassle of local installations and configurations. Here’s where AWS Cloud Shell can be a perfect fit.

Implementation of Serverless Application Development with AWS Cloud Shell

Launch AWS Cloud Shell: Log in to the AWS Management Console, navigate to the AWS CloudShell service, and click on “Open Cloud Shell” to launch the Cloud Shell environment.

Seamless Integration with AWS Services: AWS Cloud Shell comes pre-configured with AWS CLI tools and SDKs, allowing you to interact with AWS services seamlessly. You can create and manage AWS resources, configure permissions, and test your serverless application without leaving the Cloud Shell environment.

Write and Deploy Serverless Functions: Use the pre-installed Python version in AWS Cloud Shell to write AWS Lambda functions. You can leverage the rich Python ecosystem and serverless frameworks like AWS SAM (Serverless Application Model) to develop and deploy your functions quickly.

Interact with AWS Services: AWS Cloud Shell provides direct access to AWS services through the command line. For example, you can use AWS CLI commands to create and manage S3 buckets, and DynamoDB tables, or deploy API Gateway endpoints. This eliminates the need for manual configuration and simplifies the development and testing of serverless applications.

Enhancements

Collaborative Development: AWS Cloud Shell allows you to collaborate with your team members by sharing the Cloud Shell environment. Multiple developers can work simultaneously, leveraging the same set of resources and configurations, enhancing team productivity and efficiency.

Enhanced Python Version: As mentioned in the previous sections, you must have an idea of how to upgrade the Python version from cloud shell AWS to access the latest language features and libraries, improving your serverless application development experience.

Implementing AWS Cloud Shell for serverless application development provides numerous benefits, including a pre-configured environment, seamless integration with AWS services, enhanced collaboration, and the ability to upgrade the Python version. By leveraging these features, developers can focus on writing code, testing, and deploying serverless functions without worrying about local setup or configurations.

AWS Cloud Shell streamlines the development process, reduces context-switching, and enhances productivity, making it an excellent choice for developing serverless applications on AWS.

Use Case 2: Infrastructure Management and Automation with AWS Cloud Shell and Upgrading Python Version

Managing and automating infrastructure tasks are essential for efficient cloud operations. AWS Cloud Shell provides a centralized and convenient environment for infrastructure management and automation, allowing you to execute commands, scripts, and AWS CLI operations.

Scenario

Suppose you are a DevOps engineer responsible for managing a complex infrastructure on AWS. You need a reliable and accessible environment to perform tasks such as provisioning resources, configuring security policies, and automating infrastructure workflows.

Implementation and Upgrading Python Version from Cloud Shell AWS

AWS CLI and SDK Integration: AWS Cloud Shell comes pre-installed with the AWS CLI and SDKs, enabling you to interact with AWS services directly from the terminal. You can write scripts, execute CLI commands, and use SDKs in various programming languages (Python, JavaScript, etc.) for infrastructure management.

Resource Provisioning: Use AWS Cloud Shell to provision and manage AWS resources efficiently. You can create EC2 instances, manage Amazon RDS databases, configure security groups, set up VPCs, and perform other infrastructure-related tasks using AWS CLI commands or AWS SDKs.

Infrastructure Automation: Leverage AWS Cloud Shell to write and execute automation scripts using tools like AWS CloudFormation or AWS CDK (Cloud Development Kit). You can define infrastructure as code and deploy and manage resources in a consistent and repeatable manner.

Resource Monitoring and Troubleshooting: AWS Cloud Shell allows you to monitor resources and troubleshoot issues directly from the command line. You can use AWS CLI commands to retrieve resource information, monitor CloudWatch metrics, inspect logs, and perform diagnostic tasks, enhancing your operational efficiency.

By using AWS Cloud Shell for infrastructure management and automation, you can streamline your workflows, automate repetitive tasks, and improve overall productivity. The pre-configured environment, integrated AWS CLI and SDKs, and access to AWS services make AWS Cloud Shell an excellent choice for infrastructure management on AWS.

As we have learnt more about where and how we can use AWS Cloud Shell and Python programming, it comes to the question of how to upgrade the Python version from Cloud Shell AWS. Let us go with the process.

Upgrading Python Version and Setting up AWS Cloud Shell

Before getting started, make sure you have an AWS account and access to the AWS Management Console. Familiarity with basic command-line operations will also be beneficial.

1) Setting up AWS Cloud Shell:

  • Log in to your AWS Management Console.
  • Navigate to the AWS Cloud Shell service.
  • Click on “Open Cloud Shell” to launch the Cloud Shell environment.

2) Check the current installation package:

  • Once you’re in the Cloud Shell terminal, run the following command to check the currently installed Python version:
    python3 --version

3) Update the package manager:

  • Before upgrading Python, it’s a good practice to update the package manager. Run the following command to update the package manager:
    sudo yum update

4) Setting up Python 3.9:

  • AWS Cloud Shell uses the Amazon Linux operating system. To upgrade Python, you’ll need to install the desired version using the amazon-linux-extras command. For example, to install Python 3.9, use the following command:
    sudo yum install gcc openssl-devel bzip2-devel libffi-devel
    wget https://www.python.org/ftp/python/3.9.0/Python-3.9.0.tgz
    tar xzf Python-3.9.0.tgz
    cd Python-3.9.0
    ./configure --enable-optimizations
    sudo make altinstall

5) Confirm if the setup was successful:

  • After the installation is complete, run the following command to set up pyenv:
    curl https://pyenv.run | bash
    export PYENV_ROOT="$HOME/.pyenv"
    export PATH="$PYENV_ROOT/bin:$PATH"
    eval "$(pyenv init --path)"
    eval "$(pyenv init -)"
    eval "$(pyenv virtualenv-init -)"

6) Set the new version as the default:

  • If you want to make the newly installed Python version the default, you can choose from the pyenv list. Run the following commands to choose the new Python version:
    pyenv install --list

Select the required version from the list and run the command like below:

    sudo yum install gcc make patch zlib-devel bzip2 bzip2-devel readline-devel sqlite sqlite-devel openssl11-devel tk-devel libffi-devel xz-devel
    pyenv install 3.9.0
pyenv versions
pyenv global 3.9.0
python3 --version

Now you have successfully set your new updated Python version in AWS Cloud Shell.

Alternative Tools for Python Version Management in Data Engineering

Upgrading Python isn’t the only trick you need for handling complex data engineering projects in AWS Cloud Shell. Sometimes, you want to test new code, avoid update headaches, or keep your main Python install squeaky clean. That’s where tools like virtualenv Conda and Docker come in. Each offers unique ways to juggle multiple Python versions or create safe workspaces, so you never have to worry about bumpy upgrades breaking your workflows or team projects.

Let’s break down two of the most reliable alternatives and why you might want to use them, even if you’ve already updated Python in your Cloud Shell.

Using virtualenv in Cloud Shell

If you’ve worked in data engineering for more than five minutes, you know conflicts happen — especially when old scripts need an old library, and that shiny new project wants the latest release.

That’s when virtualenv shines. It lets you spin up separate “mini-Python environments”— each with its interpreter, packages, and dependencies. No cross-contamination between projects. Here’s how it fits into a real workflow:

  • Create a workspace with whatever Python version you want, even if it’s different from your system default.
  • Freeze a set of dependencies for your project, so you never get bitten by a library update.
  • Work in parallel on different projects, each with special package needs, without any fuss.

Steps are straightforward:

  1. Install virtualenv: Run pip install virtualenv If it’s not already there.
  2. Create a new environment with your desired version: virtualenv -p python3.11 myenv
  3. Activate it: source myenv/bin/activate
  4. Now, every package install and script runs in that bubble — nothing outside changes unless you want it to.

virtualenv is simple and light. For small teams or solo projects, it lets you move fast without drama. Want a practical look at building a working data pipeline using best practices for isolation? See the step-by-step guide on building an Automated Data Extraction Pipeline.

Why You Might Prefer conda or Docker

But sometimes, virtualenv alone just isn’t enough. Maybe you need to install data science packages with tricky system dependencies (looking at you, Conda). Maybe you want full isolation down to the OS and hardware level (cue Docker).

Conda stands out for these reasons:

  • It handles not only Python versions, but also binary dependencies. Perfect for data engineering libraries like numpy, pandas, and TensorFlow that sometimes need OS-level support.
  • Cross-platform environments — Windows, macOS, Linux, you name it.
  • You can easily spin up or clone project setups, making it easy for teams to stay in sync, especially when onboarding fast.

Typical steps:

  1. Install Miniconda or Anaconda.
  2. Create a new environment: conda create -n myenv python=3.11
  3. Activate and manage it just like you would with virtualenv, but with greater reach.

Docker amps it up even further. It’s not just about Python — it’s about capturing entire apps, system libraries, and all. Docker helps teams set up reproducible environments for massive data workflows. Why does that matter?

  • Your code runs the same everywhere — Cloud Shell, local laptops, production servers.
  • You define everything in a Dockerfile (instructions on how to build your Python environment), then share or deploy it anywhere.
  • If you need to run Spark, PostgreSQL, and your data pipeline in one repeatable “box,” Docker is your friend.

For data engineering teams who need guaranteed consistency, Docker delivers. Typing a single docker run command gives you your full toolkit, exactly as you expect.

While there isn’t a one-size-fits-all answer, knowing when to use virtualenv (quick, simple environments), Conda (cross-language and legacy dependencies) or Docker (maximum reproducibility) lets you take on any project in AWS Cloud Shell without sweating Python upgrades.

If you’re juggling multiple Python projects or need advice on which Python management strategy fits your team, consider how each tool matches your workflow and what you’re building. This flexibility is what sets apart efficient data engineering from chaotic guesswork — pick your tool, set your boundaries, and keep those upgrades painless.

Running Python via Docker in Cloud Shell

When you’re working with data engineering tasks in AWS Cloud Shell, sometimes you just want a quick, clean way to run your code without worrying about underlying system settings. This is where Docker steps in. Docker lets you pack your Python app into a tiny “container” that runs the same anywhere — locally, in your cloud shell, or on someone else’s box. It’s not as scary as it sounds. You get isolation, reproducibility, and a safety net, all in one shot. Let’s jump right in and see how using Docker can change the game for your Python projects.

Lightweight Docker Setup for Python

Spinning up a Python container in Cloud Shell is easier than you might think. Here’s how I do it, step by step — no fuss, no mess.

  1. Make sure Docker is available: In AWS Cloud Shell, you may need to start Docker first. Run:
    • sudo service docker start
  2. Create a folder for your app:
    • mkdir my-python-app && cd my-python-app
  3. Write your Python script:
    • Save your code as app.py. For example:print("Hello from Docker Python!")
  4. Add a Dockerfile to describe your setup: The Dockerfile tells Docker exactly what you need.FROM python:3.11 COPY app.py . CMD ["python", "app.py"]
  5. Build your Docker image:
    • docker build -t python-test .
  6. Run your app in a container:
    • docker run --rm python-test

Your output should read: Hello from Docker Python! That’s it — your app runs inside its environment, with Python 3.11 and nothing extra. Need a different version or want to add libraries? Just change your FROM line or tweak the Dockerfile.

Running data engineering scripts in a container keeps things tidy and avoids accidental conflicts with the system Python. For more about how others spin up lightweight Python containers, this Stack Overflow thread walks through running a Python script with Docker and Google Cloud Run.

Pros and Cons of Container-Based Environments

Like any tool, Docker brings both upsides and tradeoffs. Here’s what you need to know if you’re considering containers for your daily data engineering work.

Why containers rock:

  • Consistency. Your code and libraries don’t change from machine to machine. What you tested is exactly what ships.
  • Isolation. No more “it worked on my laptop but broke in production.” You get a walled garden for each task.
  • Easy sharing. Packing your code and all dependencies is as easy as sending a Docker image.
  • Quick reset. Break your environment? Toss it and restart — no need for a fresh VM or a new Cloud Shell session.

What to watch for:

  • Learning curve. If you’re new to Docker, expect to spend a little time up front. But once you’re over the hump, it’s just another tool.
  • Resource use. Containers are lightweight, but running many at once can eat up memory or disk, especially on shared Cloud Shell instances.
  • Complexity. More moving parts mean more to debug when things break.
  • Build times. Large images with lots of libraries can take a while to build and push.

For repeatable data engineering workflows, containers can save time and sanity. But they’re not always the right answer, especially for fast-and-loose experiments. If you want to dig deeper into managing Python environments, including virtualenv and Conda comparisons, you can see a practical rundown in this internal guide on the key differences between Python and Anaconda environments.

Bottom line? Try running Python in a Docker container the next time you want a clean run for a data job in Cloud Shell. You’ll likely spend less time fixing weird errors and more time focusing on your real data engineering work. If you’re interested in scheduling or automating Python tasks with Docker, this community thread on automating a Python script with Docker and Google Cloud shows how teams are using these tools for repeatable, automated jobs.

Security Considerations When Installing Python Manually in Cloud Shell

Stepping into manual Python installs on AWS Cloud Shell? It gives you control, sure, but it also opens doors to things you might not want — like security holes and messy package conflicts. It’s important to keep your data engineering work tight and trustworthy. Here’s what you need to know to play it smart and stay safe.

Avoiding Vulnerabilities and Version Conflicts

Manual installs mean more responsibility on your plate. If you’re grabbing Python from random sources or skipping security checks, you’re asking for trouble. Outdated installers, unofficial binaries, or skipping signature checks can lead to malware or unpatched bugs sneaking in. Always use the official Python releases from python.org or your distro’s trusted repos. This cuts down on risk fast.

A fresh Python install sounds clean, but in a cloud shell, where you don’t always control the base system, it’s easy to break things or leave openings for attackers. Missing updates mean vulnerabilities stick around. For example, running abandoned versions like Python 2 can leave the door wide open for exploits because security holes never get patched. If this sounds familiar, it’s because this is a key reason to avoid installing old interpreters in new cloud setups, as pointed out in this piece on why you shouldn’t install Python 2 directly on EC2.

Best tips to stay secure when you’re manually installing:

  • Use official, signed binaries. Avoid scripts or install guides from forums unless you trust the source.
  • Double-check install scripts. Don’t just curl and pipe something into bash unless you see what it does.
  • Stay updated. As soon as a vulnerability patch drops, update Python and your packages. Outdated tools mean easier attacks.
  • Don’t forget permissions. Limit install actions to the directories and users needed—don’t go full admin unless there’s no other way.
  • Isolate when possible. If you work with sensitive data or pipelines, use containers or virtual environments to avoid system-wide chaos.

Finally, in shared or managed environments, your Cloud Shell may have restrictions for a reason. Trying to bypass them with manual compiles can break compliance rules or start version wars across your team. Checking with your IT or security folks first can save a lot of grief.

Managing Package Dependencies Safely

Once you’ve got Python set up, the job isn’t done — now you face the beast of dependency management. In data engineering projects, one rogue package update can break your whole workflow. Installing something globally in Cloud Shell means every user and every process can feel that change…good or bad.

Use these habits to keep your packages — and your project — safe:

  • Virtual environments are your friend. Every new data engineering project gets its bubble. This way, when you pip install something new, you’re not stepping on existing toes.
  • Pin your dependencies. Write down the exact versions that work. Use a requirements.txt file — no guessing or letting pip pull the latest every time. You want to return to your work weeks later and have everything still work.
  • Use modern tools. For even more control (and less pain), tools like Pipenv or Poetry make life easier. The official tutorial on managing dependencies in Python will walk you through this if you want to level up your reliability.
  • Lock down permissions. Some packages want access to system files or run post-install scripts. Stay alert—know what each package does before you say yes.
  • Review for supply chain risk. Only use reputable libraries from known sources. If you’re doing anything with sensitive data, check for recent vulnerabilities or tampering in your key dependencies, as stressed in this guide about Python data supply chain security.

For more hands-on advice on setting up isolated environments and troubleshooting dependency messes, take a look at the practical guide on working with Python environments and packages in data workflows.

Manual Python installs in Cloud Shell aren’t dangerous if you take these steps seriously — they just need your attention and a few good habits. Keep it official, keep it isolated, and keep your dependency notes sharp. That’s how you stay productive — and out of trouble — in data engineering.

FAQs on How to Upgrade Python Version from Cloud Shell AWS

Question: What is AWS Cloud Shell?

Answer: AWS Cloud Shell is a web-based, interactive shell environment provided by Amazon Web Services (AWS). It allows users to access a pre-configured Linux environment with common development tools directly from the AWS Management Console.

Question: Which Python version is pre-installed in AWS Cloud Shell?

Answer: AWS Cloud Shell comes with Python 3 pre-installed by default. The specific version might vary, so it’s essential to check the Cloud Shell environment for the exact version.

Question: Why would I need to upgrade the Python version in AWS Cloud Shell?

Answer: You might need to upgrade the Python version to access the latest features, bug fixes, and security updates, or to ensure compatibility with certain Python applications and libraries that require a specific version.

Question: Can I have multiple Python versions in AWS Cloud Shell?

Answer: AWS Cloud Shell is a shared environment, and its system-level Python version is set globally. As a user, you cannot install multiple Python versions at the system level. However, you can use virtual environments to manage different Python versions on a per-project basis.


Question: How do I host a web app on AWS EC2?

Answer: To host a web application (Web app) on AWS EC2, follow these general steps:

Step 1: Launch an EC2 Instance

Step 2: Connect to your EC2 instance using SSH (Secure Shell) or RDP (Remote Desktop Protocol) for Windows instances. Install the necessary web server software like Apache, Nginx, or any other server that supports your application’s programming language (e.g., Node.js, Python, etc.). Deploy your web application code to the server and configure it accordingly.

Step 3: Apply security best practices, such as enabling firewalls, restricting access, and setting up SSL certificates for secure communication.

Step 4: Set up DNS (Domain Name System) records to point to your EC2 instance’s IP address.

Step 5: Monitor the performance of your Web app using AWS monitoring tools or third-party monitoring solutions.

Question: How do I host a dynamic website on AWS EC2?
Answer: Hosting a dynamic website on AWS EC2 is similar to hosting a regular website with a few additional considerations for dynamic content. Here are the steps:

Step 1: Decide on the web server software and programming language that supports your dynamic website requirements (e.g., Apache with PHP, Nginx with Node.js, etc.).

Step 2: Set up a database to store and manage the dynamic content. AWS offers services like Amazon RDS (Relational Database Service) or Amazon DynamoDB for this purpose.

Step 3: Launch an EC2 instance as described in the previous answer. Install the necessary web server and configure it to support your chosen programming language.

Step 4: Upload your dynamic website files to the EC2 instance. Ensure the web server communicates with the database for fetching and storing dynamic data.

Step 5: Follow security best practices to secure your dynamic website and the associated database. Configure the domain name and DNS records to point to your EC2 instance’s IP address.

Questions on Python Upgrade

Question: What is a virtual environment, and how can I create one in AWS Cloud Shell?

Answer: A virtual environment is an isolated Python environment that allows you to install packages and dependencies specific to a project without affecting the system-wide Python installation. You can create one using the following steps:

  • Install the desired Python version using pyenv or other methods.
  • Create a virtual environment with virtualenv or the built-in venv module.
  • Activate the virtual environment to use the desired Python version for your project.

Question: How can I manage Python packages in AWS Cloud Shell?

Answer: You can use the pip package manager to install, update, and manage Python packages within your virtual environment.

Question: Is there a risk of breaking Cloud Shell if I upgrade Python?

Answer: Upgrading the system-level Python version in AWS Cloud Shell can cause compatibility issues with other AWS tools and services that rely on the default Python version. It’s generally recommended to use virtual environments to manage Python versions to avoid system-wide changes.

Should We Upgrade Or Not?

Now that you are equipped and have gained knowledge on how to upgrade Python version from Cloud Shell AWS, you can confidently utilize the latest Python features and enhancements for your AWS projects. Take advantage of the upgraded Python version to develop powerful applications and leverage the full potential of AWS Cloud Shell.

Happy coding with your upgraded Python version in AWS Cloud Shell!