Data Engineering

A shared workspace for code, experiments, and data pipelines.

Projects

• Project 1 — Reading CSV Files with Pandas — load a real aqueous-solubility dataset (AQSolDB) into a pandas DataFrame and explore it with shape, dtypes, summary stats, and filtering.
• Project 2 — Summary Statistics & Outlier Detection — compute quartiles and the IQR and implement Tukey's outlier rule from scratch on the Palmer Penguins dataset, discovering why outliers only surface once you group by species.

Prerequisites/Setup

1. Homebrew

The macOS package manager — used to install everything below. If you don't have it:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

2. Git

macOS ships git with Apple's Command Line Tools:

xcode-select --install     # installs git + compilers (skip if already present)
git --version              # verify

Optional: brew install git for a newer version than Apple's.

3. uv

uv manages the Python interpreter, the virtual environment, and packages — all in one fast tool.

brew install uv

Getting started

git clone [email protected]:SuperCowPowers/data_engineering.git
cd data_engineering
uv sync          # creates .venv, installs the right Python + all dependencies

uv sync reads pyproject.toml and .python-version, downloads Python 3.13 if you don't have it, and builds the environment. That's the whole setup.

Running code

uv run python path/to/script.py                # run a script

Prefer the classic workflow? Activate the env and use python directly:

source .venv/bin/activate
python path/to/script.py

Editor setup

Point your editor at the project's .venv so it uses the right interpreter and finds the installed packages.

PyCharm

1. Settings → Project → Python Interpreter → Add Interpreter → Add Local.
2. Choose Existing and select .venv/bin/python in the project. (PyCharm 2024.2+ also has a native uv option that does this for you.)

VS Code

1. Install the Python extension.
2. Command Palette (⌘⇧P) → Python: Select Interpreter → pick the one under .venv. VS Code usually auto-detects it on open.

Tests

uv run pytest            # run tests

Contributing (pull request flow)

git checkout -b my-feature
# ... make changes, commit ...
git push -u origin my-feature

Then open a pull request on GitHub for review.

Layout

data_engineering/
├── pyproject.toml          # project, dependencies, tool config
├── .python-version         # pinned Python version
├── uv.lock                 # exact resolved versions (created by `uv sync`)
├── src/data_engineering/   # importable, shared code
├── tests/                  # pytest tests
├── project_1/              # reading CSVs with pandas
└── project_2/              # summary statistics & outlier detection