I have ~7,000 2 line dbf files (zonal stats output) that I would like merged into one table, all the same format. I have used the arcpy merge tool, as seen below.

    #merge tables together

    outfile = "C:\\Pivot\\hourly.dbf"
    arcpy.env.workspace = "C:\\Pivot\\dbfs"
    listTable = arcpy.ListTables()
    arcpy.Merge_management(listTable, outfile)
    print("All done!")

and it works but INCREDIBLY slowly. Adding about one file every 10 minutes. Is there a way to concatenate these files using a dbf module? Or is there a reason this is functioning so slowly? I've already processed all of the dbfs a couple times with other tools and they weren't nearly this slowly.

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  • Is it same slow using GUI? – FelixIP Jan 26 '16 at 19:44
  • Do all of your tables have the exact same schema (field names, types)? – CStarbird Jan 26 '16 at 20:57

If your 7000 files have the exact same number of columns you can easily do that without arcpy in a few steps:
- Fetch a list with the name of your files
- Open a first file as binary
- Fetch the length of the header of this file (it will be used for each other files, assuming they have the same number of records/columns, as you say it is an output of statistical information) and read its data
- Read the data of each other file..
- Export the merged file (as .csv for example)

import numpy as np
import struct
import os

# Fetch the name of each one of your files
file_list = [i for i in os.listdir('.') if '.dbf' in i]

# Use a first file to fetch the header length and the number of fields:
first_file = file_list.pop()
with open(first_file, 'rb') as f:
    # num_rec, len_header and num_fields are read
    #  thanks to a recipe written by Raymond Hettinger:
    num_rec, len_header = struct.unpack('<xxxxLH22x', f.read(32))
    num_fields = (len_header - 33) // 32
    data = np.array(f.read().split()).reshape((num_rec, num_fields))

# Loop on the other to fetch their values and concatenate them:
for file_name in file_list:
    with open(file_name, 'rb') as f:
        data = np.concatenate((
            data, np.array(f.read().split()).reshape((num_rec, num_fields))

np.savetxt("merged_stats.csv", data, delimiter=",")

Note that it will only work if your files have the same number of columns and records (and the same data-type in it, in fact due to the split method it assumes that your values aren't strings with spaces in it).
Anyway I guess it can easily be adapted to better fit your need, by reading the number of records on each file for example.

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