![]() ![]() ![]() Jupyter Notebooks: Code and documentation combined # In this book, we will be using Jupyter Notebooks - computational notebooks developed in the Project Jupyter 1.įigure 1.8. Clearly, a solution that would combine documentation of the code with the code itself would be quite helpful! Computational notebooks offer a solution to this problem through combining text, code, results, graphics and much more all in one file. Among natural scientists, this often means that the documentation simply never gets written, or that it does not keep up with changes in the software, which may mean the documentation has little value. Second, documenting the functionality of software outside of the source code requires programmers to take an additional step to write documentation separately, and also to keep the documentation in sync with the code as its functionality changes. This may be difficult for non-programmers. First, documentation in source code files relies on users being able to access and read the source code. software engineers use), however there are drawbacks. There is nothing inherently wrong with this approach (it’s the approach that e.g. Documenting the code is done either as text comments within the source code files or separately in a word processed document or a website. To be able to run these script files, you need to execute them separately via a terminal, or call the functionalities from Python interpreter. The traditional approach to writing programs comprises writing the actual program in source code files which are normally plain text files having the file extension. ![]() Writing and running Python code # Python script files: the traditional way of writing code # ![]() Interpreting topographic features from raster data Multimodal spatial accessibility analysis with Python Inverse Distance Weighting interpolation with Python Retrieving data from Web Coverage Service (WCS) Retrieving data from Web Feature Service (WFS) Raster operations between multiple layers Introduction to raster processing with Python Preparing GeoDataFrames from geographic data Introduction to spatial data analysis with geopandas Introduction to geographic data objects in Python Part II - Introduction to GIS with Python Quickly getting started (without installing Python) ![]()
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