Over time, Python has become the programming language of choice for spatial analysis, resulting in many packages that read, convert, analyze, and visualize spatial data. With so many packages available, it made sense to create a reference book for students and experienced professionals containing essential geospatial Python libraries for Python 3.
This book also comes at an exciting moment: new technology is transforming how people work with geospatial data – IoT, machine learning, and data science are areas where geospatial data is used constantly. This explains the inclusion of new Python libraries, such as CARTOframes and MapboxGL, and Jupyter is included as well, to explore these new trends. At the same time, web and cloud-based GIS is increasingly becoming the new standard. This is reflected in the chapters of the second part of this book, where interactive geospatial web maps and REST APIs are introduced.
These newer libraries are combined with a number of older ones that have become essential over the years, and are still very popular to this day, such as Shapely, Rasterio, and GeoPandas. Readers who are new to this field will be given a proper introduction to popular libraries, putting them into perspective and comparing their syntax through code examples using real-world data.
Finally, this books marks the transition from Python 2 to 3.x. All of the libraries covered in this book were written in Python 3.x so that the readers can access all of them using Jupyter Notebook, which is also the recommended Python coding environment for this book.
