NumPy Array Iterating

Iterating Arrays

Iterating means going through elements one by one.
As we deal with multi-dimensional arrays in numpy, we can do this using basic for loop of python.
If we iterate on a 1-D array it will go through each element one by one.

import numpy as np

arr = np.array([1, 2, 3])

for x in arr:
  print(x)

Iterating 2-D Arrays

In a 2-D array it will go through all the rows.

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

for x in arr:
  print(x)

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

for x in arr:
  for y in x:
    print(y)

Iterating 3-D Arrays

In a 3-D array it will go through all the 2-D arrays.

import numpy as np

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])

for x in arr:
  print(x)

Iterating down to the scalars:

import numpy as np

arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])

for x in arr:
  for y in x:
    for z in y:
      print(z)

Iterating Arrays Using nditer()

The function nditer() is a helping function that can be used from very basic to very advanced iterations. It solves some basic issues which we face in iteration, let's go through it with examples.

Iterating on Each Scalar Element

import numpy as np

arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

for x in np.nditer(arr):
  print(x)

Iterating Array With Different Data Types

We can use op_dtypes argument and pass it the expected datatype to change the datatype of elements while iterating. NumPy needs a buffer to perform this action, so we pass flags=['buffered'].

import numpy as np

arr = np.array([1, 2, 3])

for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']):
  print(x)

Iterating With Different Step Size

import numpy as np

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

for x in np.nditer(arr[:, ::2]):
  print(x)

Enumerated Iteration Using ndenumerate()

Enumeration means mentioning sequence number of somethings one by one.

import numpy as np

arr = np.array([1, 2, 3])

for idx, x in np.ndenumerate(arr):
  print(idx, x)

import numpy as np

arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])

for idx, x in np.ndenumerate(arr):
  print(idx, x)