Iterating means going through elements one by one.<\/p>\n\n\n\n
As we deal with multi-dimensional arrays in numpy, we can do this using basic If we iterate on a 1-D array it will go through each element one by one.<\/p>\n\n\n\n Iterate on the elements of the following 1-D array:<\/p>\n\n\n\n Try it Yourself \u00bb<\/a><\/p>\n\n\n\n In a 2-D array it will go through all the rows.<\/p>\n\n\n\n Iterate on the elements of the following 2-D array:<\/p>\n\n\n\n If we iterate on a n<\/em>-D array it will go through n-1th dimension one by one.<\/p>\n\n\n\n To return the actual values, the scalars, we have to iterate the arrays in each dimension.<\/p>\n\n\n\n Iterate on each scalar element of the 2-D array:<\/p>\n\n\n\n Try it Yourself \u00bb<\/a><\/p>\n\n\n\n In a 3-D array it will go through all the 2-D arrays.<\/p>\n\n\n\n Iterate on the elements of the following 3-D array:<\/p>\n\n\n\n Try it Yourself \u00bb<\/a><\/p>\n\n\n\n To return the actual values, the scalars, we have to iterate the arrays in each dimension.<\/p>\n\n\n\n Iterate down to the scalars:<\/p>\n\n\n\n Try it Yourself \u00bb<\/a><\/p>\n\n\n\n The function In basic Iterate through the following 3-D array:<\/p>\n\n\n\nfor<\/code> loop of python.<\/p>\n\n\n\nExampleGet your own Python Server<\/a><\/h3>\n\n\n\n
import numpy as np
arr = np.array([1, 2, 3])
for x in arr:
print(x)<\/code><\/pre>\n\n\n\n
\n\n\n\nIterating 2-D Arrays<\/h2>\n\n\n\n
Example<\/h3>\n\n\n\n
import\u00a0numpy\u00a0as\u00a0np
arr = np.array([[1,\u00a02,\u00a03], [4,\u00a05,\u00a06]])
for\u00a0x\u00a0in\u00a0arr:
\u00a0\u00a0print(x)<\/code><\/pre>\n\n\n\nExample<\/h3>\n\n\n\n
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
for x in arr:
for y in x:
print(y)<\/code><\/pre>\n\n\n\n
\n\n\n\n
\n\n\n\nIterating 3-D Arrays<\/h2>\n\n\n\n
Example<\/h3>\n\n\n\n
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)<\/code><\/pre>\n\n\n\nExample<\/h3>\n\n\n\n
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)<\/code><\/pre>\n\n\n\n
\n\n\n\nIterating Arrays Using nditer()<\/h2>\n\n\n\n
nditer()<\/code> 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.<\/p>\n\n\n\nIterating on Each Scalar Element<\/h3>\n\n\n\n
for<\/code> loops, iterating through each scalar of an array we need to use n<\/em> for<\/code> loops which can be difficult to write for arrays with very high dimensionality.<\/p>\n\n\n\nExample<\/h3>\n\n\n\n
import numpy as np
arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
for x in np.nditer(arr):
print(x)<\/code><\/pre>\n\n\n\n