Dan_Patterson

Numpy Snippets # 2 .... array to table and back again ...

Blog Post created by Dan_Patterson Champion on Sep 9, 2016

Numpy Snippets

 

Updates: 2016-09-09

 

This just a quick example of how to use existing arrays and export them to tables.  In this case I will use arcpy functionality to produce a dBase file.  Numpy can be used directly to produce text files as well.

To begin with:

  • two arrays of X and Y values are created in the range 0-10 inclusive (ie 11 numbers),
  • a data type is specified (dt)... in this case I assigned 'X' and 'Y' to the columns and specified a 64-bit floating point number,
  • an array, XY, is create using some fancy zipping of the original arrays with the specified data type,
  • ArcPy is imported, an output table is created using the data access module's NumPyArrayToTable.
  • Now for the magical reveal

 

>>> import numpy as np
>>> X = np.arange(11)                # take some numbers
>>> Y = np.arange(11)                # ditto
>>> dt = [('X','<f8'),('Y','<f8')]   # specify a data type ie 64 bit floats
>>>
>>> XY = np.array(zip(X,Y), dtype = dt) # create a 2D array of floats
>>> XY
array([(0.0, 0.0), (1.0, 1.0), (2.0, 2.0), (3.0, 3.0), (4.0, 4.0),
       (5.0, 5.0), (6.0, 6.0), (7.0, 7.0), (8.0, 8.0), (9.0, 9.0),
       (10.0, 10.0)],
      dtype=[('X', '<f8'), ('Y', '<f8')])
>>>
>>> import arcpy                     # now lets do some arcpy stuff
>>> out_table = 'c:/temp/test.dbf'
>>> arcpy.da.NumPyArrayToTable(XY,out_table)

 

: -----------------------------------------------------

Now for the reveal...

 

Output_Table.jpg

 

: -----------------------------------------------------

Bring it back you say?   Nothing could be easier.

>>> in_array = arcpy.da.TableToNumPyArray(out_table,['OID','X','Y'])
>>> in_array
array([(0, 0.0, 0.0), (1, 1.0, 1.0), (2, 2.0, 2.0), (3, 3.0, 3.0),
       (4, 4.0, 4.0), (5, 5.0, 5.0), (6, 6.0, 6.0), (7, 7.0, 7.0),
       (8, 8.0, 8.0), (9, 9.0, 9.0), (10, 10.0, 10.0)],
      dtype=[('OID', '<i4'), ('X', '<f8'), ('Y', '<f8')])

: -----------------------------------------------------

Out to *.csv you say?  Too easy (nothing fancy this time...just the numbers but formatted a bit).

 

    0,      0.00,      0.00
    1,      1.00,      1.00
    2,      2.00,      2.00
    3,      3.00,      3.00
    4,      4.00,      4.00
    5,      5.00,      5.00
    6,      6.00,      6.00
    7,      7.00,      7.00
    8,      8.00,      8.00
    9,      9.00,      9.00
   10,     10.00,     10.00

 

So NumPy and ArcPy do play 'nice' together.  Experiment a bit.   More later.

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