How to access different rows of a multidimensional NumPy array?

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
# Importing Numpy moduleimport numpy as np  # Creating a 3X3 2-D Numpy arrayarr = np.array([[10, 20, 30],                 [40, 5, 66],                 [70, 88, 94]])  print("Given Array :")print(arr)  # Access the First and Last rows of arrayres_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
# Importing Numpy moduleimport numpy as np  # Creating a 3X4 2-D Numpy arrayarr = np.array([[101, 20, 3, 10],                 [40, 5, 66, 7],                 [70, 88, 9, 141]])                 print("Given Array :")print(arr)  # Access the Middle row of arrayres_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
# Importing Numpy moduleimport numpy as np  # Creating a 4X4 2-D Numpy arrayarr = 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 Last three rows of arrayres_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
# Importing Numpy moduleimport numpy as np  # Creating a 5X4 2-D Numpy arrayarr = 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)  # Access the First two rows of arrayres_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
# Importing Numpy module import numpy as np  # Creating 3-D Numpy arrayn_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)  # Access the Middle rows of 3-D arrayres_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
# Importing Numpy module import numpy as np  # Creating 3-D Numpy arrayn_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)  # Access the First and Last rows of 3-D arrayres_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.
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