A beginner-friendly, notebook-based course covering the Python skills you need for Earth-observation (EO) data analysis — from core syntax to satellite data cubes in the cloud.
- You have little or no Python experience and want to work with satellite data.
- You are comfortable using a laptop, a browser, and an editor / terminal.
- You want to build toward xarray data cubes and STAC catalogs.
| Requirement | Details |
|---|---|
| Python | ≥ 3.10 |
| Editor | VS Code with the Jupyter extension, or JupyterLab |
| OS | macOS, Linux, or Windows (WSL recommended) |
No prior EO experience is required — key terms are explained in the Glossary.
| # | Notebook | Topic | Key skills |
|---|---|---|---|
| 01 | 01_python_basics.ipynb | Python & Jupyter fundamentals | Cells, variables, types, f-strings, strings, lists, dicts, if/for, functions, paths, JSON |
| 02 | 02_data_analysis_basics.ipynb | pandas + NumPy + Matplotlib | read_csv, filter, groupby, merge, arrays, shapes, masking, raster thinking, histogram, scatter, imshow |
| 03 | 03_xarray_eo.ipynb | xarray for EO data cubes | Labelled dimensions, coordinates, selection, computation, plotting |
| 04 | 04_stac_fundamentals.ipynb | STAC catalogs & search | STAC concepts, pystac-client, Planetary Computer, browsing items & assets |
| 05 | 05_stac_xarray_satellite_data.ipynb | From STAC to analysis-ready cubes | stackstac, loading Sentinel-2, cloud masking, NDVI time series |
| # | Notebook | Topic |
|---|---|---|
| A1 | A1_cloud_data_formats.ipynb | COG, rasterio, cloud-native formats |
| A2 | A2_optional_dask_parallel.ipynb | Dask for parallel / out-of-core processing |
git clone <repo-url>cd eo_python_foundationspython3 -m venv .venv
source .venv/bin/activate # macOS / Linux# .venv\Scripts\activate # Windowspython -m pip install --upgrade pip
python -m pip install -r requirements.txtVS Code: Open the folder and select the .venv kernel in a notebook.
JupyterLab:
jupyter labeo_python_foundations/
├── README.md ← You are here
├── GLOSSARY.md ← Key EO & Python terms
├── requirements.txt ← All Python dependencies
├── .gitignore
├── assets/img/ ← Images used by notebooks
├── data/ ← Small bundled datasets
│ ├── eo_scene_catalog.csv
│ └── eo_ndvi_stack.npz
├── notebooks/ ← The course notebooks
│ ├── 01_python_basics.ipynb
│ ├── 02_data_analysis_basics.ipynb
│ ├── 03_xarray_eo.ipynb
│ ├── 04_stac_fundamentals.ipynb
│ ├── 05_stac_xarray_satellite_data.ipynb
│ ├── A1_cloud_data_formats.ipynb
│ └── A2_optional_dask_parallel.ipynb
├── outputs/ ← Generated at runtime (git-ignored)
└── scripts/ ← Build / maintenance scripts (internal)
- Short explanations → runnable code → exercises in every section.
- ✅ Try it exercises with
<details>solutions (click to reveal). - 🧠 Checkpoint quizzes to consolidate understanding.
⚠️ Common mistakes called out inline.- Notebooks are designed to be run top-to-bottom in order.
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