Professional Python project: inspect data and plan experiments.
This course builds capabilities through working projects. Durable skills are grounded in real work: setting up a professional environment, reading and running code, understanding the logic, and pushing work to a shared repository. Each example is a professional Python project.
Complete Workflow A: Set Up Your Machine in pro-analytics-02.
Analysts often begin by opening tabular data in a tool such as Excel and looking through the rows and columns.
Python is another popular way to work with data. Python lets us write instructions once and run as needed. Both Excel and Python let us inspect data, describe it, and start thinking about what we might do with it.
This project introduces Python through a common data analytics task:
- Explore the Palmer Penguins dataset first in Excel.
- Then use Python to load, inspect, and describe the data.
The project introduces several important ideas:
- Grain - what one row of data represents.
- Columns - the information available about each observation.
- Target - something we might want to predict later.
- Feature - information that might help predict the target.
- Rationale - why we think a feature might be useful.
Python functions enable writing reusable instructions and then calling the functions with different information.
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Analyst Data Description (and Possible Prediction Plan):
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A row represents: one penguin
A target we might predict: body_mass_g
A feature that might help: bill_length_mm
Why the feature might help:
A bigger penguin probably has both a longer bill and more mass,
so bill length might help predict body mass.
That is, a longer bill may indicate a larger body mass.
- data/ - the CSV data file
- docs/ - the project narrative and documentation
- src/datafun/app.py - the Python logic
- zensical.toml - documentation site config
Follow the step-by-step workflow guide carefully.
Why? Because getting a Python project running your machine requires many parts working together - and once it runs, it makes everything else possible.
Challenges are expected. Sometimes instructions may not quite match your operating system. When issues occur, share screenshots, error messages, and details about what you tried. Working through issues is part of implementing professional projects.
After completing Phase 1. Start & Run, you'll have this example project
running on your machine.
A new file project.log will appear in the root project folder
and running the example script will print out:
===================================
END main() - Executed successfully!
===================================The commands below are used in the workflow guide above. They are provided here for convenience.
Follow the guide for the full instructions.
Show command reference
Open a machine terminal in your Repos folder,
change directory (cd) into the new folder,
and run code . to open only this project in VS Code:
git clone https://github.com/denisecase/datafun-01-foundations
cd datafun-01-foundations
code .When VS Code opens, accept the Extension Recommendations
(click Install All or similar when asked).
To set up a local project Python environment (managed by uv)
and align VS Code with it, run the following commands.
These are listed for convenience. For best results, follow the detailed instructions in pro-analytics-02 guide.
Use VS Code menu option Terminal / New Terminal to open a VS Code terminal
in the root project folder.
Copy each command, paste into your terminal, and hit ENTER,
to run each command one at a time.
uv self update
uv python pin 3.14
uv python install
uv lock --upgrade
uv syncIf asked: "We noticed a new environment has been created.
Do you want to select it for the workspace folder?" Click "Yes".
If successful, you'll see a new .venv folder appear in the root project folder.
Install and run pre-commit checks (twice if necessary as shown below):
uv run pre-commit install
uv run pre-commit autoupdate
git add -A
uv run pre-commit run --all-files
# repeat if changes were made by pre-commit tasks
uv run pre-commit run --all-filesVS Code should have only this project open.
Open a VS Code terminal (menu: Terminal / New Terminal) and run:
git pull
# run the module
uv run python -m datafun.app
# do chores
uv run ruff format .
uv run ruff check . --fix
uv run ty check
uv run python -m pytest
uv run python -m zensical buildWhile editing project code and docs, repeat the commands above to run files, check them, and rebuild docs as needed.
Save progress frequently.
Some tools may make changes;
you may need to re-run git add and commit
to ensure everything gets committed before pushing.
git add -A
git commit -m "your message here"
# repeat if changes were made (try the UP ARROW)
git add -A
git commit -m "your message here"
git push -u origin main- Use the UP ARROW and DOWN ARROW in the terminal to scroll through past commands.
- Use
CTRL+fto find (and replace) text within a file.
- You do not need to add to or modify
tests/. Tests are recommended and provided for example only. - Many files are silent helpers. Explore as you like, but most files are never touched.
- You do NOT need to understand everything; let understanding build over time.
If VS Code does not automatically use the new .venv environment:
- Open the Command Palette (
Ctrl+Shift+P). - Run Python: Select Interpreter.
- Select the interpreter from this project's
.venvfolder.
If VS Code still does not recognize the environment or newly installed tools:
- Open the Command Palette (
Ctrl+Shift+P). - Run Developer: Reload Window.
If you see something like this in your terminal: >>> or ...
You accidentally started Python interactive mode.
It happens.
Press Ctrl c (both keys together) or Ctrl+Z then Enter on Windows.
This project is licensed under the MIT License.