Frequently, I have a need to both explicitly define input data schemas for processing using PySpark and simultaneously include the schema in Sphinx documentation. Usually I can get the schema for loading without too much difficulty by outputting df.schema in a Jupyter Notebook, and copy-paste into my loading method.
Functionality is continually added to ArcGIS Pro. When creating Python scripts taking advantage of new functionality, it is helpful to be able to check the version to either use an alternative method, or at least provide an error message informing the end user of needing to upgrade. Fortunately, ArcPy provides
Outdoor enthusiasts are inherently geographers and storytellers. Wandering around outside requires some level of geographic literacy just to get there and back. It does not take more than a few minutes with anybody who loves the outdoors to discover reminiscing about experiences, telling stories, also is an inherent part of
A tremendous amount of information is available through arcpy.Describe. Until ArcGIS Pro 2.8 though, figuring out what properties were available for the object you had just described required digging into the documentation pretty deep. Now, it is much easier to access these properties introspectively as a dictionary. Since
ArcGIS Pro, when initially released, included an incredibly powerful capability, the included Python environment is a Conda environment. This unlocks the extremely powerful and vast universe of installable packages available through Conda. Further, using Conda environments enables ensuring a project can be successfully moved to another machine, and successfully run
Although first and foremost a Geographer, after transitioning to the GeoAI Business Development Team at Esri, now I spend most of my time in Jupyter Lab. Although not altogther difficult, if following best practices, when installing other packages and modifying the primary Conda environment shipped with ArcGIS Pro, arcgispro-py3, you