Latest Contributions by DanPatterson

Replace the numbers below with anything you want. lst = [ [[1, 2, 3], [4, 5]], [[1, 2]], [1, 2], [[]], [[1, 2, 3], [4, 5], [6, 7, 8]], [[[1, 2, 3], [4, 5]], [[6, 7, 8]]], ]   Flatten with recursion (a function calling itself 'recursively') def flatten(a_list, flat_list=None): """Change the isinstance as appropriate. : Flatten an object using recursion : see: itertools.chain() for an alternate method of flattening. """ if flat_list is None: flat_list = [] for item in a_list: if isinstance(item, list): flatten(item, flat_list) else: flat_list.append(item) return flat_list   Simple flattening flatten(lst) [1, 2, 3, 4, 5, 1, 2, 1, 2, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8] Who says you can't start out with initial values. flatten(lst, ['a', 'b'])) ['a', 'b', 1, 2, 3, 4, 5, 1, 2, 1, 2, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8] ... View more

It seemed like a simple idea at the time. Take the outputs from another data source, concatenate the values to a string and toss them into a new field. Looks nice eh? table_of_data.png Well, well, you can even query the whole stringy-thing.  Don't get me wrong, the whole Select By Attributes and the sql thing are pretty cool table_query.png Sorting is fun too.  Who would have thought that arcpy-base had so many friends (dependencies). arcpy-base.png That is when I asked myself what are the interdependencies/requirements/dependencies for Spyder, my favorite python IDE.   Sql away, the sort thing got me to roll. That is when I thought .... what about this package?  how about that one? Time for a big summary, which wasn't going to work with the existing options. That is when I remembered those arc angels that coded the numpy stuff!!   (not the post stevie ray band or things divinical) TableToNumPyArray  and NumPyArrayToTable. Follow along.  1    import arcpy of course   or   2-4  specify your table of interest and its field(s).  We now have an array.  None of this "for row in cursor stuff".         'vals' is a view of the data in 'arr', so we can explore it and manipulate it 6     list comprehensions can be fun for every value (v) in the array (vals) split the value at the commas  (note I used "," instead of ", " since there was a mixture strip off any leading/trailing spaces to be on the safe side  7   flatten the whole dataset   8   get the unique entries and their counts.  Remember the field was one big messy string/former list thing.  9- 11  Create the output array and send the whole summary back to Pro import arcpy tbl = r"C:\arcpro_npg\Project_npg\npgeom.gdb\dep_required_by" arr = arcpy.da.TableToNumPyArray(tbl, "Required_by") vals = arr["Required_by"] big = [i.strip() for v in vals for i in v.split(",")] big_flat = npg.flatten(big) uniq1, cnts1 = np.unique(big_flat, return_counts=True) out_tbl = r"C:\arcpro_npg\Project_npg\npgeom.gdb\dep_summary" out_arr = np.asarray(list(zip(uniq1, cnts1)), dtype=[('Package', 'U50'), ('Counts', 'i4')]) arcpy.da.NumPyArrayToTable(out_arr, out_tbl)   Now the next time I am interested in exploring the python package infrastructure for Pro, I have a workflow. table_summary.png Don't be afraid to use what was given to you.  Arcpy and NumPy play nice together.       ... View more