Numpy arrays and slicing comprehension issue

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Numpy arrays and slicing comprehension issue

paul.carrico

 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]


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Re: Numpy arrays and slicing comprehension issue

Jaime Fernández del Río
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]


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Re: Numpy arrays and slicing comprehension issue

Daπid

On 8 July 2017 at 13:03, Jaime Fernández del Río <[hidden email]> wrote:
The last index is exclusive:
[a:b] means a <= index < b.

And the consequence is that the length of your array is b - a, so [:3] gives you the first 3 values.

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