D
Daniel Fetchinson
So far I was working under the assumption that the numpy array
implementation can be used as a drop-in replacement for native python
lists, i.e. wherever I see a list 'a' and I want to speed up my
numerical calculations I just replace it with 'numpy.array( a )' and
everything will work just as before. It took me about half a day to
track down a bug which was a result of this assumption being wrong.
The fact that the following two outputs are not the same is a bug or a
feature of numpy?
##### I would have thought the two array outputs would be the same ######
import numpy
a = [ [ 0, 0 ], [ 1, 0 ], [ 1, 1 ] ]
pythonarray = a
pythonarray.sort( )
print pythonarray
numpyarray = numpy.array( a )
numpyarray.sort( )
print numpyarray
#################################################################
implementation can be used as a drop-in replacement for native python
lists, i.e. wherever I see a list 'a' and I want to speed up my
numerical calculations I just replace it with 'numpy.array( a )' and
everything will work just as before. It took me about half a day to
track down a bug which was a result of this assumption being wrong.
The fact that the following two outputs are not the same is a bug or a
feature of numpy?
##### I would have thought the two array outputs would be the same ######
import numpy
a = [ [ 0, 0 ], [ 1, 0 ], [ 1, 1 ] ]
pythonarray = a
pythonarray.sort( )
print pythonarray
numpyarray = numpy.array( a )
numpyarray.sort( )
print numpyarray
#################################################################