How to access different rows of a multidimensional NumPy array?
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
# 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
# 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
# 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
# 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
# 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
# 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)
Output:
In the above example, we access and print the First and Last rows of the 3-D NumPy array.
