Instead of having 39 tables and trying to relate them individually, it sounds like what you want to do is combine/denormalize your various CSV files into one. Then simply import that into a geodatabase table or DBF and relate it to your point layer. If the filename is significant you could add it as a column to the combined CSV file.
You might be able to do this simply using the ArcToolbox Merge tool, but if you want a bit more flexibility (such as adding the filename as a column like I mentioned), the merge_csv_files function in the following Python script, which makes use of the very handy csv module, can do this for you.
The script, if run on its own as written, will run through a standalone test case (the if __name__ == "__main__" section) to demonstrate its use (see output below). Feel free to adapt it to your needs. For example you could import it as a module or copy-paste the merge_csv_files function into your own script.
import os, sys, csv, pprint, tempfile
def merge_csv_files(input_csv_files, output_csv_file, filename_column=None):
"""
Merges each of the CSV files specified by the input_csv_files sequence into
the output_csv_file, optionally mapping the filename (minus extension) of
each input_csv_file into a new column specified by filename_column.
"""
with open(output_csv_file, "wb") as fo:
w = None
for i, csvfile in enumerate(input_csv_files):
if filename_column:
filename = os.path.basename(os.path.splitext(csvfile)[0])
with open(csvfile, "rb") as fi:
r = csv.DictReader(fi)
if filename_column:
r.fieldnames.append(filename_column)
if i < 1:
w = csv.DictWriter(fo, r.fieldnames)
w.writerow(dict((fn,fn) for fn in r.fieldnames)) # Python 2.6
## w.writeheader() # Python 2.7+
for row in r:
if filename_column:
row[filename_column] = filename
w.writerow(row)
return output_csv_file
def write_csv_file(csv_file, rows):
"""Writes the input rows to the output csv_file."""
with open(csv_file, "wb") as f:
w = csv.writer(f)
w.writerows(rows)
return csv_file
def pprint_csv_file(csv_file):
"""Reads the input csv_file and pretty-prints its contents."""
with open(csv_file, "rb") as f:
r = csv.DictReader(f)
pprint.pprint(r.fieldnames)
for row in r:
pprint.pprint([row[key] for key in r.fieldnames])
if __name__ == "__main__":
# Get the path to the user's temporary files directory
tempdir = tempfile.gettempdir()
# Create a temporary CSV file for testing
testcsv1 = write_csv_file(
os.path.join(tempdir, "W00006-1.csv"),
[["WELL_ID", "WATER_LEVEL"],
[1, 50.5],
[2, 60.5],
[3, 70.5]])
# Create another temporary CSV file for testing
testcsv2 = write_csv_file(
os.path.join(tempdir, "W00006-2.csv"),
[["WELL_ID", "WATER_LEVEL"],
[4, 55.5],
[5, 65.5],
[6, 75.5]])
# Merge the two CSV files
testmergecsv = merge_csv_files(
[testcsv1, testcsv2],
os.path.join(tempdir, "WELL_WATER_LEVELS.csv"),
"FILE_NAME")
# Print all three CSV files
pprint_csv_file(testcsv1)
pprint_csv_file(testcsv2)
pprint_csv_file(testmergecsv)
# Clean up temporary files
[os.remove(f) for f in [testcsv1, testcsv2, testmergecsv]]
Example test output:
['WELL_ID', 'WATER_LEVEL']
['1', '50.5']
['2', '60.5']
['3', '70.5']
['WELL_ID', 'WATER_LEVEL']
['4', '55.5']
['5', '65.5']
['6', '75.5']
['WELL_ID', 'WATER_LEVEL', 'FILE_NAME']
['1', '50.5', 'W00006-1']
['2', '60.5', 'W00006-1']
['3', '70.5', 'W00006-1']
['4', '55.5', 'W00006-2']
['5', '65.5', 'W00006-2']
['6', '75.5', 'W00006-2']
The merged CSV file can be imported into a geodatabase table and related to your points feature class on WELL_ID. Actually in 10.1 you can relate to the CSV file directly without having to import it to a GDB table. Not sure about 10.0. For best performance, you'll probably want to import it to a file geodatabase and create attribute indices, however.
