Python Climate Data Analysis

Python Climate Data Analysis#

Welcome! This site provides step-by-step tutorials, sample code, and workflows for analyzing climate data using Python. It is designed for students, researchers, and professionals working with observational datasets and climate reanalysis products.

🧭 What You’ll Find Here#

This book is organized into four main sections:

  1. Setup – How to install Miniconda, configure your environment, and launch JupyterLab.

  2. Read Data – Techniques for loading climate data from NetCDF, CSV, and remote sources.

  3. Data Analysis – Examples of climate statistics, seasonal averages, and anomaly calculations.

  4. Visualization – Methods for plotting maps, time series, and climate diagnostics using xarray, matplotlib, and GeoCAT.

🌐 Getting Started#

To begin, follow the Setup instructions to configure your Python environment.

If you are new to Jupyter notebooks or working on a remote server, don’t worry — we walk you through every step.

📬 Questions or Feedback?#

This site is under active development as a resource for students and collaborators. Some sections may be incomplete or evolving, but we aim to provide accurate and useful guidance as we build it out. If you notice any issues or have suggestions, feel free to reach out or open an issue on the GitHub repository.

Contents