Prerequisite: Numpy module
The following article discusses how we can access different columns of multidimensional Numpy array. Here, we are using Slicing method to obtain the required functionality.
Example 1: (Accessing the First and Last column of Numpy array)
# Importing Numpy module import numpy as np
# Creating a 3x3 Numpy array arr = np.array([[ 11 , 20 , 3 ],
[ 89 , 5 , 66 ],
[ 71 , 88 , 39 ]])
print ( "Given Array :" )
print (arr)
# Access the First and Last column of array res_arr = arr[:,[ 0 , 2 ]]
print ( "\nAccessed Columns :" )
print (res_arr)
|
Output:
Given Array :
[[11 20 3]
[89 5 66]
[71 88 39]]
Accessed Columns :
[[11 3]
[89 66]
[71 39]]
Example 2: (Accessing the Middle and Last column of Numpy array)
# Importing Numpy module import numpy as np
# Creating a 4x4 Numpy array arr = np.array([[ 1 , 20 , 3 , 1 ],
[ 40 , 5 , 66 , 7 ],
[ 70 , 88 , 9 , 11 ],
[ 80 , 100 , 50 , 77 ]])
print ( "Given Array :" )
print (arr)
# Access the Middle and Last column of array res_arr = arr[:,[ 1 , 3 ]]
print ( "\nAccessed Columns :" )
print (res_arr)
|
Output:
Given Array :
[[ 1 20 3 1]
[ 40 5 66 7]
[ 70 88 9 11]
[ 80 100 50 77]]
Accessed Columns :
[[ 20 1]
[ 5 7]
[ 88 11]
[100 77]]
Example 3: (Accessing the Last two columns of Numpy array)
# Importing Numpy module import numpy as np
# Creating a 3d (3X4X4) Numpy array arr = np.array([[[ 21 , 20 , 3 , 1 ],
[ 40 , 5 , 66 , 7 ],
[ 70 , 88 , 9 , 11 ],
[ 80 , 100 , 50 , 77 ]],
[[ 65 , 120 , 53 , 73 ],
[ 49 , 50 , 56 , 11 ],
[ 81 , 88 , 34 , 22 ],
[ 564 , 56 , 76 , 99 ]],
[[ 45 , 85 , 38 , 455 ],
[ 40 , 53 , 69 , 6 ],
[ 50 , 528 , 654 , 11 ],
[ 54 , 87 , 78 , 77 ]]])
print ( "Given Array :" )
print (arr)
# Access the Last two columns of array res_arr = arr[ 2 ,:,[ 2 , 3 ]]
print ( "\nAccessed Columns :" )
print (res_arr)
|
Output:
Given Array :
[[[ 21 20 3 1]
[ 40 5 66 7]
[ 70 88 9 11]
[ 80 100 50 77]]
[[ 65 120 53 73]
[ 49 50 56 11]
[ 81 88 34 22]
[564 56 76 99]]
[[ 45 85 38 455]
[ 40 53 69 6]
[ 50 528 654 11]
[ 54 87 78 77]]]
Accessed Columns :
[[ 38 69 654 78]
[455 6 11 77]]
Example 4: (Accessing the First column of a 4D Numpy array)
# Importing Numpy module import numpy as np
# Creating a 4D Numpy array arr = np.array([
[
[
[ 1 , 2 ],
[ 3 , 4 ]
],
[
[ 5 , 6 ],
[ 7 , 8 ]
]
],
[
[
[ 9 , 10 ],
[ 11 , 12 ]
],
[
[ 13 , 14 ],
[ 15 , 16 ]
]
]
]) print ( "Given Array :" )
print (arr)
# Access the First three columns of array res_arr = arr[ 0 , 0 ,:,[ 0 ]]
print ( "\nAccessed Columns :" )
print (res_arr)
|
Output:
Given Array :
[[[[ 1 2]
[ 3 4]]
[[ 5 6]
[ 7 8]]]
[[[ 9 10]
[11 12]]
[[13 14]
[15 16]]]]
Accessed Columns :
[[1 3]]