Sometimes we need to combine 1-D and 2-D arrays and display their elements. Numpy has a function named as numpy.nditer(), which provides this facility.
Syntax: numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order=’K’, casting=’safe’, op_axes=None, itershape=None, buffersize=0)
Example 1:
Python3
# importing Numpy package import numpy as np num_1d = np.arange(5) print("One dimensional array:") print(num_1d) num_2d = np.arange(10).reshape(2,5) print("\nTwo dimensional array:") print(num_2d) # Combine 1-D and 2-D arrays and display # their elements using numpy.nditer() for a, b in np.nditer([num_1d, num_2d]): print("%d:%d" % (a, b),) |
Output:
One dimensional array: [0 1 2 3 4] Two dimensional array: [[0 1 2 3 4] [5 6 7 8 9]] 0:0 1:1 2:2 3:3 4:4 0:5 1:6 2:7 3:8 4:9
Example 2:
Python3
# importing Numpy package import numpy as np num_1d = np.arange(7) print("One dimensional array:") print(num_1d) num_2d = np.arange(21).reshape(3,7) print("\nTwo dimensional array:") print(num_2d) # Combine 1-D and 2-D arrays and display # their elements using numpy.nditer() for a, b in np.nditer([num_1d, num_2d]): print("%d:%d" % (a, b),) |
Output:
One dimensional array: [0 1 2 3 4 5 6] Two dimensional array: [[ 0 1 2 3 4 5 6] [ 7 8 9 10 11 12 13] [14 15 16 17 18 19 20]] 0:0 1:1 2:2 3:3 4:4 5:5 6:6 0:7 1:8 2:9 3:10 4:11 5:12 6:13 0:14 1:15 2:16 3:17 4:18 5:19 6:20
Example 3:
Python3
# importing Numpy package import numpy as np num_1d = np.arange(2) print("One dimensional array:") print(num_1d) num_2d = np.arange(12).reshape(6,2) print("\nTwo dimensional array:") print(num_2d) # Combine 1-D and 2-D arrays and display # their elements using numpy.nditer() for a, b in np.nditer([num_1d, num_2d]): print("%d:%d" % (a, b),) |
Output:
One dimensional array: [0 1] Two dimensional array: [[ 0 1] [ 2 3] [ 4 5] [ 6 7] [ 8 9] [10 11]] 0:0 1:1 0:2 1:3 0:4 1:5 0:6 1:7 0:8 1:9 0:10 1:11
