Numpy - Reversing Rows and Columns

Lecture 7:- Numpy - Reversing Rows and Columns

In NumPy, you can reverse the rows and columns of an array using simple slicing operations. Here's how you can reverse the rows and columns of a NumPy array:

1. Reverse Rows: To reverse the rows of an array, you can use slicing with a step of -1 along the first axis (axis=0). This will reverse the order of rows in the array.

   pythonCopy code
   import numpy as np
   
   # Original 2D array
   original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
   
   # Reversed rows
   reversed_rows_array = original_array[::-1, :]
   
   # Output
   print(reversed_rows_array)
   

   Output:

   luaCopy code
   [[7 8 9]
    [4 5 6]
    [1 2 3]]
   

2. Reverse Columns: To reverse the columns of an array, you can use slicing with a step of -1 along the second axis (axis=1). This will reverse the order of columns in the array.

   pythonCopy code
   import numpy as np
   
   # Original 2D array
   original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
   
   # Reversed columns
   reversed_columns_array = original_array[:, ::-1]
   
   # Output
   print(reversed_columns_array)
   

   Output:

   luaCopy code
   [[3 2 1]
    [6 5 4]
    [9 8 7]]
   

3. Reverse Rows and Columns: To reverse both the rows and columns simultaneously, you can use both row and column slicing with a step of -1.

   pythonCopy code
   import numpy as np
   
   # Original 2D array
   original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
   
   # Reversed rows and columns
   reversed_rows_columns_array = original_array[::-1, ::-1]
   
   # Output
   print(reversed_rows_columns_array)
   

   Output:

   luaCopy code
   [[9 8 7]
    [6 5 4]
    [3 2 1]]
   

In all of the above examples, the [::-1] slicing reverses the order along the specified axis (rows in the case of reverse rows and columns in the case of reverse columns). Keep in mind that these operations create a new array; the original array remains unchanged. If you want to modify the original array in place, you can use the np.flip function with appropriate axis arguments.

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