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NumPy - Iterating Over Array
  • 时间：2024-12-22

NumPy - Iterating Over Array

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NumPy package contains an iterator object numpy.nditer. It is an efficient multidimensional iterator object using which it is possible to iterate over an array. Each element of an array is visited using Python’s standard Iterator interface.

Let us create a 3X4 array using arange() function and iterate over it using nditer.

Example 1

import numpy as np
a = np.arange(0,60,5)
a = a.reshape(3,4)

print  Original array is: 
print a
print  
 

print  Modified array is: 
for x in np.nditer(a):
   print x,

The output of this program is as follows −

Original array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Modified array is:
0 5 10 15 20 25 30 35 40 45 50 55

Example 2

The order of iteration is chosen to match the memory layout of an array, without considering a particular ordering. This can be seen by iterating over the transpose of the above array.

import numpy as np 
a = np.arange(0,60,5) 
a = a.reshape(3,4) 
   
print  Original array is: 
print a 
print  
   
   
print  Transpose of the original array is:  
b = a.T 
print b 
print  
   
   
print  Modified array is:  
for x in np.nditer(b): 
   print x,

The output of the above program is as follows −

Original array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Transpose of the original array is:
[[ 0 20 40]
 [ 5 25 45]
 [10 30 50]
 [15 35 55]]

Modified array is:
0 5 10 15 20 25 30 35 40 45 50 55

Iteration Order

If the same elements are stored using F-style order, the iterator chooses the more efficient way of iterating over an array.

Example 1

import numpy as np
a = np.arange(0,60,5)
a = a.reshape(3,4)
print  Original array is: 
print a
print  
 

print  Transpose of the original array is: 
b = a.T
print b
print  
 

print  Sorted in C-style order: 
c = b.copy(order =  C )
print c
for x in np.nditer(c):
   print x,

print  
 

print  Sorted in F-style order: 
c = b.copy(order =  F )
print c
for x in np.nditer(c):
   print x,

Its output would be as follows −

Original array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Transpose of the original array is:
[[ 0 20 40]
 [ 5 25 45]
 [10 30 50]
 [15 35 55]]

Sorted in C-style order:
[[ 0 20 40]
 [ 5 25 45]
 [10 30 50]
 [15 35 55]]
0 20 40 5 25 45 10 30 50 15 35 55

Sorted in F-style order:
[[ 0 20 40]
 [ 5 25 45]
 [10 30 50]
 [15 35 55]]
0 5 10 15 20 25 30 35 40 45 50 55

Example 2

It is possible to force nditer object to use a specific order by exppcitly mentioning it.

import numpy as np 
a = np.arange(0,60,5) 
a = a.reshape(3,4) 

print  Original array is:  
print a 
print  
   

print  Sorted in C-style order:  
for x in np.nditer(a, order =  C ): 
   print x,  
print  
  

print  Sorted in F-style order:  
for x in np.nditer(a, order =  F ): 
   print x,

Its output would be −

Original array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Sorted in C-style order:
0 5 10 15 20 25 30 35 40 45 50 55

Sorted in F-style order:
0 20 40 5 25 45 10 30 50 15 35 55

Modifying Array Values

The nditer object has another optional parameter called op_flags. Its default value is read-only, but can be set to read-write or write-only mode. This will enable modifying array elements using this iterator.

Example

import numpy as np
a = np.arange(0,60,5)
a = a.reshape(3,4)
print  Original array is: 
print a
print  
 

for x in np.nditer(a, op_flags = [ readwrite ]):
   x[...] = 2*x
print  Modified array is: 
print a

Its output is as follows −

Original array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Modified array is:
[[ 0 10 20 30]
 [ 40 50 60 70]
 [ 80 90 100 110]]

External Loop

The nditer class constructor has a ‘flags’ parameter, which can take the following values −

Sr.No. Parameter & Description
1

c_index

C_order index can be tracked
2

f_index

Fortran_order index is tracked
3

multi-index

Type of indexes with one per iteration can be tracked
4

external_loop

Causes values given to be one-dimensional arrays with multiple values instead of zero-dimensional array

Example

In the following example, one-dimensional arrays corresponding to each column is traversed by the iterator.

import numpy as np 
a = np.arange(0,60,5) 
a = a.reshape(3,4) 

print  Original array is:  
print a 
print  
   

print  Modified array is:  
for x in np.nditer(a, flags = [ external_loop ], order =  F ):
   print x,

The output is as follows −

Original array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Modified array is:
[ 0 20 40] [ 5 25 45] [10 30 50] [15 35 55]

Broadcasting Iteration

If two arrays are broadcastable, a combined nditer object is able to iterate upon them concurrently. Assuming that an array a has dimension 3X4, and there is another array b of dimension 1X4, the iterator of following type is used (array b is broadcast to size of a).

Example

import numpy as np 
a = np.arange(0,60,5) 
a = a.reshape(3,4) 

print  First array is:  
print a 
print  
   

print  Second array is:  
b = np.array([1, 2, 3, 4], dtype = int) 
print b  
print  
  

print  Modified array is:  
for x,y in np.nditer([a,b]): 
   print "%d:%d" % (x,y),

Its output would be as follows −

First array is:
[[ 0 5 10 15]
 [20 25 30 35]
 [40 45 50 55]]

Second array is:
[1 2 3 4]

Modified array is:
0:1 5:2 10:3 15:4 20:1 25:2 30:3 35:4 40:1 45:2 50:3 55:4
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