Python Automatically Convert to Esri Spatially Enabled Dataframe I frequnetly need a Spatially Enabled Dataframe from a variety of sources, and these sources frequently change. Guessing there is a very good chance I am not the only one doing this - here is my solution to this as a Gist you are more than welcome to use.
Python API ArcGIS-ML-Docker Easily 80 percent of my time at work is now spent in ArcGIS Python API, either the stable branch, or much of the time, the daily build. Frequently I switch between these environments, but also need a few other Conda packages to support machine learning. To support flexibly using both
Python API ArcGIS Python Geometry from Shapely The ArcGIS Python API Geometry object includes the capability to export the geometry to a Shapely Geometry object, but not the capability to create a new ArcGIS Geometry object from a Shapely object. Thankfully, it is not difficult to add this capability to every ArcGIS Geometry object in a namespace.
Python API Loading a Previously Saved SpatialDataFrame from CSV Increasingly I find myself working with SpatialDataFrames for analysis in Jupyter Notebooks in Python. Much of the time the easiest way to save results for future work is saving to a CSV file. This CSV file can then be loaded back into another notebook as an active SpatialDataFrame for further
Python Creating a SpatialDataFrame from a Web GIS Item The relatively recent addition of the SpatialDataFrame object to the ArcGIS Python API was a gigantic leap forward. The SpatialDataFrame marries the common data structure used for data munging and data science, the Pandas DataFrame, with ArcGIS in a familiar (for data scientists at least) and user friendly environment, Jupyter
