Page - 51 - in Programming for Computations – Python - A Gentle Introduction to Numerical Simulations with Python 3.6, Volume Second Edition

2.3 NumericalPythonArrays 51 In [12]: y[0] = 10.0 In [13]: y Out[13]: array([ 10., 1., 2.]) # ...as expected In [14]: x Out[14]: array([ 10., 1., 2.]) # ...x has changed too! Intuitively, it mayseem verystrange that changingan element inycausesa similar changeinx!Thethingis,however,thatourassignmenty = xdoesnotmakeacopy of thexarray.Rather,Pythoncreatesanother reference,namedy, to thesamearray object thatx refers to. That is, there is one array object with two names (x andy). Therefore,changingeitherxory, simultaneouslychanges“theother”(notethat this behaviordiffers from what we found in Sect.2.2.3 for single integer,float or string objects). To really get a copy that is decoupled from the original array, you may use the copy functionfromnumpy, In [15]: from numpy import copy In [16]: x = linspace(0, 2, 3) # x becomes array([ 0., 1., 2.]) In [17]: y = copy(x) In [18]: y Out[18]: array([ 0., 1., 2.]) In [19]: y[0] = 10.0 In [20]: y Out[20]: array([ 10., 1., 2.]) # ...changed In [21]: x Out[21]: array([ 0., 1., 2.]) # ...unchanged 2.3.5 SlicinganArray By use of a colon, you may work with a slice of an array. For example, by writing x[i:j], we address all elements from index i (inclusive) to j (exclusive) in an arrayx.An interactivesession illustrates this, In [1]: from numpy import linspace In [2]: x = linspace(11, 16, 6) In [3]: x Out[3]: array([ 11., 12., 13., 14., 15., 16.]) In [4]: y = x[1:5] In [5]: y Out[5]: array([ 12., 13., 14., 15.])