Python

In this article, we’ll explore the core concepts that make Python one of the most used programming languages, how to install it, and the main tools used by researchers and engineers.

A Python Tour

Python, created by Guido van Rossum in the early 1990s, is a programming language known for its simplicity, readability, and versatility. Its clean syntax makes code easier to read and write compared with languages such as C, C++, and Rust.

Guido van Rossum. Credit: Michael Cavotta.

Python uses dynamic typing, which means you don’t need to specify whether a variable is a number, a string, or another type when you create it. The interpreter determines the type during runtime, making Python more flexible compared to statically typed languages like C++ or Rust.

For example, in Python, you can write:

message = "My awesome message!"

In contrast, C++ requires specifying the variable type:

std::string message = "My awesome message!";

Trade-off

Dynamic typing simplifies and speeds up coding. However, it can make debugging more challenging, as type errors only appear during runtime. This trade-off highlights the balance between ease of use and debugging complexity.

Another feature of Python is its clean and readable syntax, which often requires fewer lines of code compared to other programming languages.

For instance, printing “Hello, World!” in Python is as straightforward as:

print("Hello, World!")

In C++, even a simple program like this requires additional code:

#include <iostream>

int main() {
    cout << "Hello, World!" << endl;
    return 0;
}

Python is an interpreted language, which means its code is executed line by line without the need for a prior compilation step. This allows for immediate testing and debugging but can result in slower execution speeds compared to compiled languages like C++ or Rust, where code is translated into machine code before execution. While compilation adds an extra step, it often results in faster runtime performance.

Strengths and Weaknesses

Each programming language has its own strengths and weaknesses. Python is a great choice for AI, web development, and rapid prototyping. While no language is perfect for every task, mastering one language can provide a solid foundation for learning others.

Installation and Considerations

There are several ways to install Python. I recommend using the uv package manager, which can help you with tasks such as:

• Installing and managing specific Python versions
• Creating and managing virtual environments
• Installing and managing Python packages and tools
• Replacing several tools, including conda, pyenv, pip, poetry, virtualenv, and more

Installing uv

To install uv, open the Terminal or PowerShell and paste the following code:

linux

curl -LsSf https://astral.sh/uv/install.sh | sh

macOs

brew install uv

Windows

powershell -ExecutionPolicy ByPass -c `
"irm https://astral.sh/uv/install.ps1 | iex"

Tip

For more details or help with uv, visit the official documentation at docs.astral.sh/uv.

Installing Python

To install Python, enter the following code in the Terminal or PowerShell:

1. Update the shell PATH:

   uv tool update-shell

2. Install Python:

   uv python install 3.14

Running Python Code

In this article, we’ll explore three ways to run Python code:

1. The Python interpreter for quick feedback
2. IPython for an interactive development experience
3. JupyterLab for developing complex, interactive projects

The Python Interpreter

The Python interpreter is ideal for quick testing and run simple scripts.

1. Launch the Python interpreter:

   uv run python

   You’ll see something like this:

   uv run python
   
   Python 3.14.7 (main, Sep  1 2026, 14:05:28)
   Type "help", "credits" or "license" for more information.
   
   >>>

2. Hello, world!

   Tradition suggests that the first program in a new language should print the words Hello, world! on the screen. In Python, this can be done in a single line:

   print("Hello, world!")

   Type it into the Python interpreter:

   >>> print("Hello, world!")
   Hello, world!

   Try calculating the sum of the first 5 natural numbers:

   >>> 1 + 2 + 3 + 4 + 5
   15

IPython

IPython offers code completion, syntax highlighting, and the ability to execute code line by line. It is great for interactive learning and experimentation.

1. Install IPython:

   uv tool install ipython --with pip

2. Launch IPython:

   ipython

   You’ll see something like this:

   Python 3.14.7 (main, Sep  1 2026, 14:05:28)
   IPython 9.17.1 -- An enhanced Interactive Python.
   
   In [1]:

3. Exploring Tab Completion:

   IPython’s tab completion lets you complete code with a single press of the Tab key. This simple feature reduces typos and saves time.

   For example, type pr in IPython and press Tab:

   In [1]: pr
     print()  property  %precision  %prun

   Select print( ) and complete the "Hello, world!" example:

   In [1]: print("Hello, world!")
   Hello, world!

4. Discovering Object Attribute Completion:

   In Python, attributes describe an object, while methods define what it can do. IPython’s object attribute completion makes it easy to discover both.

   For example, create a string:

   In [2]: name = "Marcos"

   Then type name. and press Tab to see the string’s available attributes and methods:

   In [3]: name.
    capitalize()  lower()  upper()  ...

JupyterLab

JupyterLab is a web application for creating interactive documents that combine code, text, equations, and rich visuals. It provides Python developers with an intuitive environment for coding and experimentation.

1. Install JupyterLab:

   uv tool install jupyterlab --with altair

   This will install JupyterLab with the Altair extension that allows us to create interactive visualizations.

   Tip

   Installing JupyterLab this way is great for learning and experimenting. For serious projects, consider setting up a virtual environment.

2. Launch JupyterLab:

   jupyter-lab

   This will launch the JupyterLab server and automatically open it in your web browser.

   

3. Ploting a sine wave:

   Here’s how to create a sine wave plot using Altair, a library for interactive visualizations. Our main focus is to present JupyterLab’s functionality, so don’t worry about understanding the code just yet.

   Create a new notebook by selecting File → New → Notebook, then paste the code below:

   import altair as alt
   
   data = alt.sequence(0, 10, 0.1, as_='x')
   
   alt.Chart(data).transform_calculate(
       y='sin(datum.x)'
   ).mark_line().encode(
       x='x:Q',
       y='y:Q',
   ).properties(
       width=768,
       height=300
   )

   To run the code, use Shift + Enter:

   

Working on projects

Create a new Python project:

uv init hello-world

Run the hello-world main.py example:

uv run ./hello-world/main.py

uv will create the following files:

• Directoryhello-world

  • .git
  • Directory.venv

    • bin
    • lib
    • pyvenv.cfg
  • .gitignore
  • main.py
  • pyproject.toml
  • README.md
  • uv.lock

Python environments

Create a virtual environment:

uv venv

You can create a virtual environment with a specific Python version:

uv venv --python 3.14

Activate the virtual environment:

source .venv/bin/activate

Install a package in the virtual environment:

uv pip install ...

List all installed packages in the virtual environment:

 uv pip list

Exit the virtual environment:

deactivate

Wrapping Up

So, that’s a quick tour of Python and the tools you’ll use to get started.

You’ve learned how Python’s simple syntax and dynamic typing make it approachable, how to install Python with uv, and how to run code with the Python interpreter, IPython, and JupyterLab. You also saw how uv can help you create projects, manage Python versions, and work with virtual environments.

There’s a lot more to Python, of course, but you don’t need to learn everything at once. Pick a small project, open your favorite environment, and start experimenting. That’s where things really start to click.

Happy coding!