How to iterate Arrays in Numpy

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In this article we are going to see how to iterate arrays in NumPy. Let’s get a head start of what array iteration is all about.

Iterating simply means checking element one at a time in a program. They are many multi-dimensional arrays in NumPy. 

Iterating a One-dimensional Array:

We are going to go through one element at a time. This is mostly done using the ‘for’ loop

import numpy as np
A = np.arange(12)
for cell in A:
    print (cell, end=' ')

OUTPUT:

Iterating a two- dimensional array

In addition to the for loop, we are going to used another function called re.shape. Here we able to iterate array each in a row.

Here is an example where the two functions are involved

import numpy as np
A = np.arange(12).reshape(4,3)
for row in A:
    print(row)

OUTPUT:

In a case where you don’t to what to iterate two rows, there is a function called flatten that reduce a two – dimensional array into a one-dimensional array. Note that this flatten functions reduce a multi-dimensional array into the specific array that you want.

Iterating Using the Nditer Function

It is a basic tool in NumPy module that we can used to solve basic problems. With this function we are able to create order during iteration by using parameters.  If you specify a particular order, the nditer functions called it, following the position of the element called.

Iterating scalar each scalar element

With the simple scalar elements, we can use the n for loops. 

Examples 

import numpy as np
arr = np.array([['a' 'b'], ['c' 'd'], ['e' 'f']])
for x in np.nditer(arr):
  print(x)
import numpy as np

OUTPUT:

However, this is not possible with scalars with high dimensions

Just as we can iterate scalars using nditer function, this function can also be used to get order during iteration which is called the nditer iteration order.

Example 

To get the ‘C’ order (horizontal order)

import numpy as np
a = np.arange(12)
print('Modified array is:') 
for x in np.nditer(a, flags = ['external_loop'], order = 'C'):
    print(x)

With this order, the array created is arranged horizontally following the order specified.

OUTPUT:

To get the Fortran order (vertical order)

import numpy as np
a = np.arange(12)
print('Modified array in F-style order:')
for x in np.nditer(a, order = 'F'): 
    print(x)

With this order, the array created is arranged vertically following the order specified

OUTPUT:

Modifying NumPy arrays 

In modifying arrays while iterating, we can use the op_flags function. So, to update the elements in an array we can do the following steps:

import numpy as np
a = np.arange(12)
for x in np.nditer(a, op_flags = ['readwrite']):
    x[...] = 5*x
print('Modified array is:')
print(a)

The op-flag tell the compiler to add 5 to the previous number. This is done in a continuous sequence

OUTPUT:

Iterating Two Arrays Simultaneously

We can use a nditer function to iterate arrays simultaneously. Here is an example to get you understand better.

import numpy as np
a = np.arange(12)
a = a.reshape(3,4) 
b = np.array([5, 6, 7, 8], dtype = int) 
print('Modified array is:')
for x,y in np.nditer([a,b]): 
    print("%d:%d" % (x,y))

Example

NumPy tells compile the run the program at the same time while following the instruction of the nditer function

OUTPUT:

As seen in this article, nditer iterator provide a basic way of touching all elements in an array. In addition to what we have seen in this article, they are more advanced iteration like Reduction Iteration, Outer Product Iteration.  Hope this session was interesting. Have a nice coding day guy