How to access different rows of a multidimensional NumPy array? (original) (raw)
Last Updated : 11 Oct, 2020
Let us see how to access different rows of a multidimensional array in NumPy. Sometimes we need to access different rows of multidimensional NumPy array-like first row, the last two rows, and even the middle two rows, etc. In NumPy , it is very easy to access any rows of a multidimensional array. All we need to do is Slicing the array according to the given conditions. Whenever we need to perform analysis, slicing plays an important role.
Case 1: In 2-Dimensional arrays
Example 1: Accessing the First and Last row of a 2-D NumPy array
Python3
import numpy as np
arr = np.array([[ 10 , 20 , 30 ],
`` [ 40 , 5 , 66 ],
`` [ 70 , 88 , 94 ]])
print ( "Given Array :" )
print (arr)
res_arr = arr[[ 0 , 2 ]]
print ( "\nAccessed Rows :" )
print (res_arr)
Output:
In the above example, we access and print the First and Last rows of the 3X3 NumPy array.
Example 2: Accessing the Middle row of 2-D NumPy array
Python3
import numpy as np
arr = np.array([[ 101 , 20 , 3 , 10 ],
`` [ 40 , 5 , 66 , 7 ],
`` [ 70 , 88 , 9 , 141 ]])
print ( "Given Array :" )
print (arr)
res_arr = arr[ 1 ]
print ( "\nAccessed Row :" )
print (res_arr)
Output:
In the above example, we access and print the Middle row of the 3X4 NumPy array.
Example 3: Accessing the Last three rows of 2-D NuNumPy py array
Python3
import numpy as np
arr = np.array([[ 1 , 20 , 3 , 1 ],
`` [ 40 , 5 , 66 , 7 ],
`` [ 70 , 88 , 9 , 11 ],
`` [ 80 , 100 , 50 , 77 ]])
print ( "Given Array :" )
print (arr)
res_arr = arr[[ 1 , 2 , 3 ]]
print ( "\nAccessed Rows :" )
print (res_arr)
Output:
In the above example, we access and print the last three rows of the 4X4 NumPy array.
Example 4: Accessing the First two rows of a 2-D NumPy array
Python3
import numpy as np
arr = np.array([[ 1 , 20 , 3 , 1 ],
`` [ 40 , 5 , 66 , 7 ],
`` [ 70 , 88 , 9 , 11 ],
`` [ 80 , 100 , 50 , 77 ],
`` [ 1 , 8.5 , 7.9 , 4.8 ]])
print ( "Given Array :" )
print (arr)
res_arr = arr[[ 0 , 1 ]]
print ( "\nAccessed Rows :" )
print (res_arr)
Output:
In the above example, we access and print the First two rows of the 5X4 NumPy array.
Case 2: In 3-Dimensional arrays
Example 1: Accessing the Middle rows of 3-D NumPy array
Python3
import numpy as np
n_arr = np.array([[[ 10 , 25 , 70 ], [ 30 , 45 , 55 ], [ 20 , 45 , 7 ]],
`` [[ 50 , 65 , 8 ], [ 70 , 85 , 10 ], [ 11 , 22 , 33 ]]])
print ( "Given 3-D Array:" )
print (n_arr)
res_arr = n_arr[:,[ 1 ]]
print ( "\nAccessed Rows :" )
print (res_arr)
Output:
In the above example, we access and print the Middle rows of the 3-D NumPy array.
Example 2: Accessing the First and Last rows of 3-D NumPy array
Python3
import numpy as np
n_arr = np.array([[[ 10 , 25 , 70 ], [ 30 , 45 , 55 ], [ 20 , 45 , 7 ]],
`` [[ 50 , 65 , 8 ], [ 70 , 85 , 10 ], [ 11 , 22 , 33 ]],
`` [[ 19 , 69 , 36 ], [ 1 , 5 , 24 ], [ 4 , 20 , 96 ]]])
print ( "Given 3-D Array:" )
print (n_arr)
res_arr = n_arr[:,[ 0 , 2 ]]
print ( "\nAccessed Rows :" )
print (res_arr)
Output:
In the above example, we access and print the First and Last rows of the 3-D NumPy array.