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.
Example
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.
Example
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x
in arr:
print(x)
Note: If we iterate on a n-D array it will go through n-1th dimension one by one.
To return the actual values, the scalars, we have to iterate the arrays in each dimension.
Example
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.
Example
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)
To return the actual values, the scalars, we have to iterate the arrays in each dimension.
Example
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, lets go through it with examples.
Iterating on Each Scalar Element
In basic for loops, iterating through each scalar of an array we need to use n for loops which can be difficult to write for arrays with very high dimensionality.
Example
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 does not change the data type of the element in-place (where the element is in array) so it needs some other space to perform this action, that extra space is called buffer, and in order to enable it in nditer() we pass flags=['buffered'].
Example
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
We can use filtering and followed by iteration.
Example
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.
Sometimes we require corresponding index of the element while iterating, the ndenumerate() method can be used for those usecases.
Example
import numpy as np
arr = np.array([1, 2, 3])
for idx, x in
np.ndenumerate(arr):
print(idx, x)
Example
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)
