Python Climate Visuals#

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Welcome! 👏

This book was originally developed for the NUS course Civil Engineering Analytics and Data Visualization.

It is a collection of online Jupyter notebooks that explicitly show students how to use Python to perform data analysis and visualization.

You can view this text online or view the source.

About the logo ✨


This book logo is composed of:
  • A skyline (Marina Bay Sands) and a landmark (Merlion) of a garden city (Singapore);
  • An unseen climate change signal revealed by warming stripes illustrating Singapore's historical (1901-2020) temperature;
  • A tag line (Python Climate Visuals) along with its acronym (pyCIVIL) indicating book contents and targeted readers.

I hope this logo could raise people's awareness of climate change and spark data-driven ideas to battle against the ongoing climate crisis.

Learning outcomes 🧰

Tutorials of this module will cover the following key skills that we think should be in civil and environmental engineers’ toolbox:

  • Data manipulation (cleaning, mining and wrangling)
  • Data visualisation (beautiful and informative graphics)
  • Basics of probability and statistics
  • Exploratory analysis on Earth and Environmental data
    • Tabular data: rows and columns (e.g., Excel spreadsheet)
    • Gridded data: multidimensional numerical arrays (e.g., raster)
  • Scientific Python and modern data science software
  • And others

Work in progress 🚧

Please note that this book website is still under active development. Contents will be added and updated frequently throughout the course. Your comments to improve the book are highly welcome!

Citation ✍️#

If you find this book useful, please cite it via:

@book{he-2021-pyCIVIL,
  title={Python Climate Visuals (pyCIVIL)},
  author={Xiaogang He},
  url={https://xiaoganghe.github.io/python-climate-visuals},
  year={2021},
  publisher={WWW}
}

Acknowledgement 🙏#

This book project would not have been possible without the contribution of the following (list alphabetically):

  • Haoling Chen

  • Meilian Li

  • Xinyue Liu

  • Zhanwei Liu

  • Zhixiao Niu

  • Huimin Wang

The contents of this book are licensed for free consumption under the following license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).