How to slice multidimensional arrays in Python
Slicing a multidimensional array includes selecting specific array parts (row, column, sections). We can easily slice multidimensional arrays using the Python library Numpy. The slicing occurs for each dimension of the array separately.
The General Syntax for slicing multidimensional arrays is:
array[start:stop:step,start:stop:step]
The first part is for row
The second part is for the column
Start: It includes an inclusive starting index.
Stop: It includes an exclusive ending index.
Step: It includes an optional step size.
Example with numpy
# import numpy as np #create a 2D array array=np.array([[15,16,17],[25,26,27],[35,36,37],[45,46,47]]) #this will generate the desired output print(array[1:3,1:3]) print(array[1:3,:1]) print(array[:,1:3]) print(array[2:3,1:]) #
Output:
# [[26 27] [36 37]] [[25] [35]] [[16 17] [26 27] [36 37] [46 47] [[36 37]] #
How to use numpy.flip() in Python
NumPy.flip() is used to flip the elements in a numpy array. It can flip the entire array or along the side(like a row or column).
Example:
#
import numpy as np
arr = np.array([[1,2,3], [4,5,6], [7,8,9]])
#flip array
arr2 = np.flip(arr)
print("original array:",arr,)
print("flipped array:", arr2)
#
Output:
# original array: [[1 2 3] [4 5 6] [7 8 9]] flipped array: [[9 8 7] [6 5 4] [3 2 1] #
Explanation
Slicing and flipping are powerful tools for working with numpy arrays. Both are useful for manipulating arrays in Python. In slicing, we extract parts of an array, and in flipping, we reverse or reorder elements.