Your Toolkit: VS Code & Jupyter

You've got Python installed and you've run a file from the terminal. That works, but typing into a bare black window gets old fast - no colours, no help, no easy way to fix a typo on line 3. A carpenter doesn't work on the floor; they set up a workbench. Let's set up yours. By the end of this chapter you'll have a proper, friendly place to write code - and you'll meet the interactive notebook that data people practically live in.

A proper home for your code: VS Code

To write code comfortably you want a code editor - a text editor that understands code, colours it in, spots your typos, and runs it at the click of a button. There are several good ones, but for a beginner the easy pick is Visual Studio Code, usually just called VS Code. It's free, it's made by Microsoft, and it's far and away the most widely used editor in the world.

Getting set up takes three steps:

1. Download it from code.visualstudio.com and install it.
2. Open VS Code, click the Extensions icon in the left bar, search for "Python", and install the official one by Microsoft. This is what gives you colours, autocomplete, and a green Run button.
3. Use File - Open Folder to open a folder for your course work. Everything you write lives there.

Running your first file in VS Code

Inside that folder, create a new file called average.py, paste in the code below, and press the Run button (the triangle in the top-right). The output appears in a panel at the bottom.

# A taste of things to come - run this in VS Code by pressing the Run button.
# We take a week of closing prices and work out the average.
prices = [101.2, 103.5, 102.8, 104.1, 105.6]   # five daily closes

average = sum(prices) / len(prices)             # add them up, divide by how many

print("Five closing prices:", prices)
print(f"Average close this week: {average:.2f}")

Five closing prices: [101.2, 103.5, 102.8, 104.1, 105.6]
Average close this week: 103.44

That's a genuine little piece of analysis: five days of closing prices, averaged to 103.44. We're using a list ([...]) and two built-in helpers, sum() and len() - all of which get their own chapters soon. For now, notice how much nicer this is than the terminal: colours, a click to run, and a tidy output panel.

Notebooks: the data explorer's playground

There's a second way to run Python that data analysts and quants adore: the Jupyter notebook. Instead of one file that runs top to bottom, a notebook is a stack of cells - little blocks of code you run one at a time, with the output appearing right underneath each one. You can run a cell, look at the result, tweak it, run it again, and build up an analysis step by step - all while keeping notes in between.

Two small things make notebooks feel magical when you're exploring data:

• Output appears inline. Run a cell that draws a chart, and the chart shows up right there in the page - no separate window.
• The last line auto-displays. In a notebook you don't even need print() - just put sum(prices) / len(prices) on the last line of a cell and the answer pops out below it (that's the Out[2]: 103.44 above).

The easiest way in is VS Code itself: install the Jupyter extension, create a file ending in .ipynb, and you've got a notebook. (Or run pip install jupyter and type jupyter notebook in your terminal for the classic browser version.)

Editor or notebook - which should I use?

Both, eventually - they're good at different jobs:

• A script (.py) is best for code you'll reuse or automate - a program you run again and again, start to finish.
• A notebook (.ipynb) is best for exploring and learning - trying ideas, inspecting data, sketching charts, keeping notes.

Try it yourself

• Open the bottom panel in VS Code (View - Terminal) and run python average.py from there too. Same result, second way to run it.
• In average.py, add a sixth price to the list, save, and re-run. Watch the average change on its own.
• If you set up a notebook, paste the three lines into separate cells and run them one by one. Notice you only needed print() in the script, not in the notebook.

Recap

• VS Code is a free, hugely popular editor; add the official Python extension and you get colours, autocomplete, and a one-click Run button.
• Always open a folder, not a lone file, and keep your practice work together.
• A Jupyter notebook runs code in cells with output shown inline - perfect for exploring data, and the last line auto-displays without print().
• Use scripts for reusable programs and notebooks for exploring; for this course, either is great.

Your workbench is ready and the tools are laid out. From here on it's all Python. In the next chapter we start with the most fundamental building block of every program there is - the variable - and we'll use it to store prices, symbols, and quantities.