A number of geoprocessing tools including Spatial Join (Analysis), Append (Management), Merge (Management), Feature Class To Feature Class (Conversion), and Table To Table (Conversion), have a parameter for controlling how fields from the input dataset(s) are processed and written, or mapped, to the output dataset – the Field Map parameter. In addition to the simple moving of attributes from input to output, field mapping can also be useful for some common tasks such as field concatenation and calculating statistics like mean, sum, and standard deviation.
If you haven’t used the Field Map before, you should! Understanding and using field mapping will often reduce the number of processing steps in a workflow, and ensure that, in any scenario, attributes are handled in an appropriate way. Yes, the Field Map parameter is a complicated one, but it is well worth the time it takes to figure it out.
Because the Field Map is a complicated parameter, working with it in Python can also be complicated. The best way to interact with field mapping in Python scripting is with the FieldMappings object. In Python, most geoprocessing tool parameter types are seen as simple numbers or strings (specifying a feature class input is as easy as providing the feature class’ path). But several of the more complex parameters have objects that exist to help you effectively work with the parameter. The Field Map parameter can accept a long structured string indicating the field map settings (you may have seen this long string equivalent in geoprocessing messages), however, working with the field mapping string is inefficient and error-prone, so use the FieldMappings object for the best experience.
I was recently presented with a data migration problem where field mappings and Python scripting literally saved me weeks of work. The goal was to convert a collection of hundreds of VPF (Vector Product Format) databases containing many feature classes to a handful of geodatabases, and because of the large scale of the migration it had to be accomplished in an automated fashion (this is where the many weeks of work would be saved). The schema of the geodatabases was already set up with a number of feature datasets and empty feature classes into which the VPF feature class data would be imported using the Append (management) tool.
The iteration through the collection of VPF databases was solved with some simple looping techniques involving the arcpy.ListDatasets() and arcpy.ListFeatureClasses() functions. However, there was a fundamental problem that nearly derailed the automation of this process: VPF feature classes can have spaces in their field names, while geodatabase datasets cannot. When the empty geodatabase feature classes were created from the schema of the VPF feature classes, the spaces in the field names were automatically changed to underscores ( _ ) in the geodatabase feature classes. This very subtle difference caused huge ripples in the automated process, since the Append (Management) tool can not automatically match fields like ‘mcc description’ to ‘mcc_description’; in the output geodatabase feature class, all the values in the ‘mcc_description’ field are NULL because the fields were not matched.
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