Combining a one and a twodimensional NumPy Array (original) (raw)
Combining a one and a two-dimensional NumPy Array
Last Updated : 01 Oct, 2020
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
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)
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
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)
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
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)
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