Python - NumPy Part 2: Array Indexing and Slicing

Efficient data access is a key feature of NumPy, enabling indexing and slicing of arrays.

Examples and Explanation

Indexing Elements

array = np.array([10, 20, 30, 40])

print(array[2])  # Output: 30

Explanation: Access elements using zero-based indexing.

Slicing Arrays

print(array[1:3])  # Output: [20 30]

Explanation: Use slicing to access a subset of the array. The start index is inclusive, and the end index is exclusive.

Indexing in 2D Arrays

matrix = np.array([[1, 2, 3], [4, 5, 6]])

print(matrix[1, 2])  # Output: 6

Explanation: Use [row, column] indexing for 2D arrays.

Slicing Rows and Columns

print(matrix[:, 1])  # Output: [2 5]

Explanation: The colon (:) selects all rows, and 1 selects the second column.

Boolean Indexing

print(array[array > 20])  # Output: [30 40]

Explanation: Apply conditions to filter elements. This feature is useful in data cleaning and analysis.