# Numpy arrays and slicing comprehension issue

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## Numpy arrays and slicing comprehension issue

 Hi Once again I need your help to understand one topic concerning slicing topic, or in other word I do not understand how it works in that particular (but common) case; I’m trying to reassign the 4 first values in an array: If I use [:3] I’m expecting to have 4 values (index 0 to 3 included) Ditto with [0:3] If I use [3:] I have 2 values as expected (indexes 3 and 4) Both code and results are presented here after, so this way of thinking worked so far in other calculations, and it fails here?  Thanks  Paul ps : extraction from the doc (https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html) [... all indices are zero-based ...]    Code: x = np.random.rand(5); print("x = ",x); ## test 1 print("partials =\n %s \nor %s \nor %s" %( x[:3], x[0:3], x[3:]) ) print("x[0] : ",x[0]); print("x[1] : ",x[1]); print("x[2] : ",x[2]); print("x[3] : ",x[3])   ## test 2 y = np.ones(4); print("y = ",y) x[0:4] = y print("x final = ",x) Provide: x =  [ 0.39921271  0.07097531  0.37044695  0.28078163  0.11590451] partials =  [ 0.39921271  0.07097531  0.37044695] or [ 0.39921271  0.07097531  0.37044695] or [ 0.28078163  0.11590451] x[0] :  0.39921271184 x[1] :  0.0709753133926 x[2] :  0.370446946245 x[3] :  0.280781629 y =  [ 1.  1.  1.  1.] x final =  [ 1.          1.          1.          1.          0.11590451] _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.python.org/mailman/listinfo/numpy-discussion
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## Re: Numpy arrays and slicing comprehension issue

 The last index is exclusive:[a:b] means a <= index < b.On Jul 8, 2017 10:20 AM, <[hidden email]> wrote:  Hi Once again I need your help to understand one topic concerning slicing topic, or in other word I do not understand how it works in that particular (but common) case; I’m trying to reassign the 4 first values in an array: If I use [:3] I’m expecting to have 4 values (index 0 to 3 included) Ditto with [0:3] If I use [3:] I have 2 values as expected (indexes 3 and 4) Both code and results are presented here after, so this way of thinking worked so far in other calculations, and it fails here?  Thanks  Paul ps : extraction from the doc (https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html) [... all indices are zero-based ...]    Code: x = np.random.rand(5); print("x = ",x); ## test 1 print("partials =\n %s \nor %s \nor %s" %( x[:3], x[0:3], x[3:]) ) print("x[0] : ",x[0]); print("x[1] : ",x[1]); print("x[2] : ",x[2]); print("x[3] : ",x[3])   ## test 2 y = np.ones(4); print("y = ",y) x[0:4] = y print("x final = ",x) Provide: x =  [ 0.39921271  0.07097531  0.37044695  0.28078163  0.11590451] partials =  [ 0.39921271  0.07097531  0.37044695] or [ 0.39921271  0.07097531  0.37044695] or [ 0.28078163  0.11590451] x[0] :  0.39921271184 x[1] :  0.0709753133926 x[2] :  0.370446946245 x[3] :  0.280781629 y =  [ 1.  1.  1.  1.] x final =  [ 1.          1.          1.          1.          0.11590451] _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.python.org/mailman/listinfo/numpy-discussion