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Visual Studio Code (VS Code) is a free, powerful code editor that has become the most popular IDE in the world. It works with virtually any programming language and is my recommended editor for Python development.

Why VS Code?

If you're going to learn Python (Yay!), I recommend using VS Code for several reasons:

• Free and open-source — No cost, no licensing hassles
• Works with everything — Python, R, Stata syntax, Markdown, LaTeX, and more
• Excellent Python support — With the Python extension, it rivals dedicated Python IDEs
• Git integration — Version control built right in
• AI coding assistants — GitHub Copilot and Claude Code integrate seamlessly
• Huge extension ecosystem — Add functionality for any use case

Installing VS Code

1. Download from code.visualstudio.com
2. Run the installer (works on Windows, Mac, and Linux)
3. Launch VS Code

The VS Code Interface

VS Code has a clean, customizable interface. The image below shows the main areas of the VS Code window. Hover over each numbered region to learn what it does:

1

2

3

4

Hover over a numbered region to learn about that part of VS Code.

Image source: VS Code Documentation

Note: If you are not seeing the terminal window, you can open it by going to the top menu and selecting View > Terminal or by pressing Ctrl + `.

Installing Extensions

VS Code's power comes from extensions. Here's how to install them:

1. Click the Extensions icon in the Activity Bar (or press Ctrl+Shift+X)
2. Search for the extension name
3. Click Install

Essential Extensions for This Course

Note: all names contain links to the extension pages with more details and descriptions.

Extensions for Other Languages

AI Coding Assistants

AI assistants can dramatically speed up your coding by suggesting completions, explaining code, and helping debug errors. Here are the main options:

Productivity & easy time interpreting code

Python Virtual Environments

Virtual environments are one of the most important concepts in Python development. If you skip this section, you will run into problems eventually. Trust me on this one.

What is a Virtual Environment?

Imagine your computer is an apartment building. Python is the building itself, and packages (like pandas, numpy, matplotlib) are furniture. Now imagine every project you work on is a different apartment in that building.

In technical terms, a virtual environment is an isolated Python installation with its own set of packages. When you activate a virtual environment, any packages you install go into that environment only, not your system-wide Python.

Why Do You Need Virtual Environments?

Here are the real-world problems that virtual environments solve:

Creating a Virtual Environment

Python has a built-in tool called venv for creating virtual environments. Here's how to use it:

Step 1: Open Terminal in VS Code

Press Ctrl + ` (backtick) to open the integrated terminal.

Step 2: Navigate to Your Project Folder

# Example: navigate to your project
cd ~/Documents/protools_er1/my_project

Step 3: Create the Virtual Environment

# Create a virtual environment named 'venv'
python -m venv venv

# Or with a more descriptive name
python -m venv my_project_env

# Create a virtual environment named 'venv'
python3 -m venv venv

# Or with a more descriptive name
python3 -m venv my_project_env

PS C:\Users\yourname\my_project> python -m venv venv
# (No output - command runs silently)

PS C:\Users\yourname\my_project> dir venv

    Directory: C:\Users\yourname\my_project\venv

Mode                 LastWriteTime         Length Name
----                 -------------         ------ ----
d-----         1/27/2026   2:45 PM                Include
d-----         1/27/2026   2:45 PM                Lib
d-----         1/27/2026   2:45 PM                Scripts
-a----         1/27/2026   2:45 PM            119 pyvenv.cfg

# Virtual environment created successfully!
# The 'venv' folder now contains your isolated Python installation.

This creates a new folder (called venv or whatever name you chose) containing a complete Python installation.

Step 4: Activate the Virtual Environment

# Activate the environment
.\venv\Scripts\Activate.ps1

# If you get an execution policy error, run this first:
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

# Activate the environment
venv\Scripts\activate.bat

# Activate the environment
source venv/bin/activate

PS C:\Users\yourname\my_project> .\venv\Scripts\Activate.ps1

(venv) PS C:\Users\yourname\my_project> _

# Notice the (venv) prefix - this confirms you're in the virtual environment!
# All packages you install now will go into this environment only.

(venv) PS C:\Users\yourname\my_project> python --version
Python 3.11.5

(venv) PS C:\Users\yourname\my_project> which python
C:\Users\yourname\my_project\venv\Scripts\python.exe

You'll know it worked when you see (venv) at the beginning of your terminal prompt:

Step 5: Install Packages

Now any packages you install will go into this virtual environment:

# Install packages (they go into the venv only)
pip install pandas numpy matplotlib statsmodels

# See what's installed in this environment
pip list

Step 6: Deactivate When Done

# Leave the virtual environment
deactivate

How to Check Which Environment You're In

There are several ways to verify your current Python environment:

# Method 1: Look at your prompt
# If you see (venv) at the start, you're in the venv

# Method 2: Check which Python is being used
which python     # Mac/Linux
where python     # Windows

# If it shows a path inside your project's venv folder, you're in the venv
# Example output: /Users/yourname/my_project/venv/bin/python

import sys
print(sys.executable)
# Shows the full path to the Python interpreter
# If it's inside your venv folder, you're in the venv

import sys
print(sys.prefix)
# Shows the base location of the Python installation

# Look at the bottom-left of VS Code
# You'll see something like: Python 3.11.5 ('venv': venv)

# Or press Ctrl+Shift+P and type "Python: Select Interpreter"
# This shows all available environments

# Terminal Check (Mac/Linux):
(venv) $ which python
/Users/yourname/my_project/venv/bin/python

# Python Check:
>>> import sys
>>> print(sys.executable)
/Users/yourname/my_project/venv/bin/python

>>> print(sys.prefix)
/Users/yourname/my_project/venv

# The path includes 'venv' - confirming you're in the virtual environment!

# VS Code Check:
# Bottom-left status bar shows: Python 3.11.5 ('venv': venv)
# Command Palette > "Python: Select Interpreter" lists all available envs

Where is the Virtual Environment Stored?

The virtual environment is simply a folder in your project directory. Here's what's inside:

Sharing Your Environment: requirements.txt

To share your project with others (or recreate it on a new computer), you save the list of installed packages:

# Save your current packages to a file
pip freeze > requirements.txt

# On a new computer, recreate the environment:
python -m venv venv
source venv/bin/activate  # or .\venv\Scripts\Activate.ps1 on Windows
pip install -r requirements.txt

The requirements.txt file looks like this:

VS Code and Virtual Environments

VS Code automatically detects virtual environments in your project. Here's how to ensure VS Code uses the right one:

1. Open your project folder in VS Code
2. Create and activate a virtual environment (as shown above)
3. Press Ctrl + Shift + P and type Python: Select Interpreter
4. Choose the interpreter that shows ('venv': venv) or your environment name

From now on, VS Code will use this environment for:

• Running Python files (F5 or Ctrl+F5)
• Running code in the terminal
• IntelliSense and autocomplete
• Jupyter notebooks

Common Virtual Environment Issues

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

Further Learning

Setting Up Your Project Folder

Before diving into data analysis, it's essential to organize your work properly. A well-structured project folder makes your research reproducible and helps you (and others) find files easily.

Recommended Folder Structure

For any research project, I recommend creating a folder structure like this:

You can create this structure using VS Code's Explorer panel or by running these commands in the terminal:

# Create project folder structure (Windows PowerShell)
mkdir data\raw, data\processed, code, notebooks, output\figures, output\tables -Force

# Create project folder structure (Mac/Linux)
mkdir -p data/raw data/processed code notebooks output/figures output/tables

import os
from pathlib import Path

# Create project folder structure
folders = [
    "data/raw",
    "data/processed",
    "code",
    "notebooks",
    "output/figures",
    "output/tables"
]

for folder in folders:
    Path(folder).mkdir(parents=True, exist_ok=True)
    print(f"Created: {folder}")

Opening Your Project in VS Code

Always open your project folder in VS Code, not individual files. This ensures:

• The Explorer panel shows all your project files
• The terminal opens in your project directory
• VS Code detects your virtual environment
• File paths work correctly

To open a folder: File → Open Folder (or Ctrl+K Ctrl+O)

Setting the Working Directory

In Python, the working directory is where Python looks for files when you use relative paths. Unlike R, Python doesn't have a simple setwd() function—instead, you use the os module.

To check your current working directory:

import os

# Where is Python looking for files?
print(os.getcwd())

To change your working directory:

import os

# Change to your project folder
os.chdir("C:/Users/yourname/Documents/my_project")

# Or use Path for cross-platform compatibility
from pathlib import Path
os.chdir(Path.home() / "Documents" / "my_project")

Practice: Download Real Data

Let's practice by downloading real economic data. Our World in Data provides excellent, well-documented datasets perfect for economics research. We'll use this data source throughout the course.

1. Visit Our World in Data Datasets on GitHub
2. Or download directly: GDP per capita (Maddison Project Database)
3. Save the file to your data/raw/ folder

Alternatively, download it directly with Python:

import urllib.request
from pathlib import Path

# Download GDP per capita data from Our World in Data
url = "https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/GDP%20per%20capita%20(Maddison%20Project%20Database%202020)/GDP%20per%20capita%20(Maddison%20Project%20Database%202020).csv"

# Create data/raw folder if it doesn't exist
Path("data/raw").mkdir(parents=True, exist_ok=True)

# Download and save
urllib.request.urlretrieve(url, "data/raw/gdp_per_capita.csv")
print("Downloaded successfully!")

Exploring Data in VS Code

Once you have data in your project, VS Code makes it easy to explore:

1. Explorer Panel: Navigate to your data/raw/ folder and click on the CSV file
2. With the Rainbow CSV or Excel Viewer extension, you'll see formatted, color-coded columns
3. Load and View: Use Python code to load and inspect the data:

import pandas as pd

# Load the data
gdp_data = pd.read_csv("data/raw/gdp_per_capita.csv")

# Quick look at the data
print(gdp_data.head())

# Basic info about the data
print(gdp_data.info())

# Summary statistics
print(gdp_data.describe())

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