In order to illustrate my question which involves two related subscript expressions, on arrays of nested structures, I have created a short code example which is given below.

In the code example, I create a nested "structure" data type (called dtype3) and two numpy.ndarray's (called arecords and brecords) which are identical in shape and dtype. The remaining code consists of the replacement statement (lines 34 and 49) at the heart of my question and print statements.

Specifically, my question centers on my expectation that the statement on line 34 (arecords["data"][1] = data_values) would have the same effect on arecords as the statement on line 49 (brecords[1]["data"]=data_values) would have on brecords. From running the code example, this is obviously not the case. In the former case, all four of the "data" arrays of the second record in arecords are set to the values found in data_values. In the latter case, only the first "data" array of the second record in brecords is set to a value in data_values and the other three "data" values remain unchanged.

I am baffled by the latter case involving brecords. If you examine the left-hand side and righthand side of the statement on line 49 (brecords[1]["data"]=data_values), both sides have the same shape(1-dim with 4 elements) and dtype. I am having a great deal of difficulty trying to understand why the replacement statement only effects the first "data" array and not all 4 "data" arrays. What am I overlooking?

Any help in explaining this behavior would be appreciated.

Thanks, RR

CODE EXAMPLE ----------------------------------------------------------------------------

import numpy type1 = numpy.dtype([("a", numpy.int32), ("b", numpy.int32)]) type2 = numpy.dtype([("alpha", numpy.float64), ("beta", numpy.float64), ("gamma", numpy.float64)]) type3 = numpy.dtype([("header", type1), ("data", type2, 4)])

header_values = numpy.empty(1, dtype=type1) data_values = numpy.empty(4, dtype=type2) header_values["a"] = 1000 header_values["b"] = 2000 data_values["alpha"] = [11.0, 21.0, 31.0, 41.0] data_values["beta"] = [12.0, 22.0, 32.0, 42.0] data_values["gamma"] = [13.0, 23.0, 33.0, 43.0]

arecords = numpy.empty(2, dtype=type3) brecords = numpy.empty(2, dtype=type3)

print print "Case A:" print print arecords, ': arecords:'.upper() print data_values, ': data_values'.upper() print arecords["data"][1].shape, ': arecords["data"][1].shape'.upper() print arecords[1]["data"].shape, ': arecords[1]["data"].shape'.upper() print data_values.shape, ': data_values.shape' print arecords["data"][1].dtype, ': arecords["data"][1].dtype'.upper() print arecords[1]["data"].dtype, ': arecords[1]["data"].dtype'.upper() print data_values.dtype, ': data_values.dtype'.upper() arecords["header"][1] = header_values arecords["data"][1] = data_values print arecords, ': arecords:'.upper()

print print "Case B:" print print brecords, ': brecords:'.upper() print data_values, ': data_values'.upper() print brecords["data"][1].shape, ': brecords["data"][1].shape'.upper() print brecords[1]["data"].shape, ': brecords[1]["data"].shape'.upper() print data_values.shape, ': data_values.shape' print brecords["data"][1].dtype, ': brecords["data"][1].dtype'.upper() print brecords[1]["data"].dtype, ': brecords[1]["data"].dtype'.upper() print data_values.dtype, ': data_values.dtype'.upper() brecords[1]["header"] = header_values brecords[1]["data"] = data_values print brecords, ': brecords:'.upper()