How to access different rows of a multidimensional NumPy array?

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

# Importing Numpy module import numpy as np # Creating a 3X3 2-D Numpy array arr = np.array([[10, 20, 30], [40, 5, 66], [70, 88, 94]]) print("Given Array :") print(arr) # Access the First and Last rows of array 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

# Importing Numpy module import numpy as np # Creating a 3X4 2-D Numpy array arr = np.array([[101, 20, 3, 10], [40, 5, 66, 7], [70, 88, 9, 141]]) print("Given Array :") print(arr) # Access the Middle row of array 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

# Importing Numpy module import numpy as np # Creating a 4X4 2-D 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 Last three rows of array 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

# Importing Numpy module import numpy as np # Creating a 5X4 2-D Numpy array 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) # Access the First two rows of array 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

# Importing Numpy module  import numpy as np # Creating 3-D Numpy array 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) # Access the Middle rows of 3-D array 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

# Importing Numpy module  import numpy as np # Creating 3-D Numpy array 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) # Access the First and Last rows of 3-D array res_arr = n_arr[:,[0, 2]] print("\nAccessed Rows :") print(res_arr)