[Python] Handling Matrices (2D Arrays) with NumPy: Creation, Access, and Slicing

Since NumPy’s ndarray can handle multi-dimensional arrays, defining it as a 2-dimensional array allows you to express mathematical “matrices.”

I have corrected some typos in the provided code (such as nparray → np.array, and commas becoming dots) and will explain the concepts below. In matrix operations, the order of “Row, Column” and the slicing notation [row_range, col_range] are particularly important.

Executable Sample Code

The following code is a complete example of creating a 3×3 matrix and performing element access and extraction (slicing).

import numpy as np

def matrix_operations_demo():
    print("=== 1. Matrix Creation ===")
    # Create a 2D array by passing a list of lists
    # Correction: Use commas for separation and np.array
    x = np.array([
        [11, 12, 13], 
        [21, 22, 23], 
        [31, 32, 33]
    ])
    print(f"Matrix x:\n{x}")
    print(f"Shape: {x.shape}") # (3, 3) -> 3 rows, 3 columns

    print("\n=== 2. Accessing Elements (Indexing) ===")
    # Syntax: x[row_index, col_index]
    # Note that it starts from 0
    
    # 0th row, 2nd column (13)
    val1 = x[0, 2]
    print(f"x[0, 2]: {val1}")
    
    # 2nd row, 0th column (31)
    val2 = x[2, 0]
    print(f"x[2, 0]: {val2}")

    print("\n=== 3. Slicing (Range Extraction) ===")
    # Syntax: x[start_row:end_row, start_col:end_col]
    # The rule is that the end index is "exclusive"
    
    # Extract 0th to 1st row (before 2), and 0th to 1st column (before 2)
    # This extracts the top-left 2x2 matrix
    sub_matrix = x[0:2, 0:2]
    print(f"x[0:2, 0:2] (Top-Left 2x2):\n{sub_matrix}")

    print("\n=== 4. Extracting Rows ===")
    # Specifying only one index retrieves the "row"
    
    # All of the 0th row
    row_0 = x[0]
    print(f"x[0] (1st Row): {row_0}")

    print("\n=== 5. Extracting Columns ===")
    # ":" means "everything"
    # Syntax: x[:, col_index] -> Means "specified column of all rows"
    
    # 1st column (The middle vertical column: 12, 22, 32)
    col_1 = x[:, 1]
    print(f"x[:, 1] (2nd Column): {col_1}")

if __name__ == "__main__":
    matrix_operations_demo()

Explanation: Rules of Index Operation

1. Specifying Coordinates x[row, col]

Unlike spreadsheet software like Excel, programming counts starting from 0.

• x[0, 0]: Top left (first element)
• x[1, 2]: 2nd row, 3rd column

2. Slicing x[start:end]

If you write 0:2, indices 0 and 1 are targeted (2 is not included). Writing just : means “everything in that dimension.”

3. Technique for Extracting Columns x[:, i]

When you want to extract only a specific “column,” the key point is to specify : (all rows) in the row designation part. This is frequently used in data analysis to “extract data for a specific item only.”