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Practice

Indexing and Slicing Arrays

After creating an array, we often want to access specific elements or select parts of it.

This is where indexing and slicing come in.


Indexing​

Indexing lets you access one item using its position.
Like Python lists, NumPy arrays start at index 0.

arr = np.array([10, 20, 30, 40])
print(arr[1])  # Output: 20

In 2D arrays, use two indices: arr[row, column].

Indexing a 2D Array
matrix = np.array([[1, 2], [3, 4]])
print(matrix[1, 0])  # Output: 3

Slicing​

Slicing lets you grab a range of values using : notation.

Slicing a 1D Array
arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])  # Output: [20 30 40]

You can also slice rows or columns from 2D arrays.

Slicing a 2D Array
matrix = np.array([[1, 2, 3], [4, 5, 6]])
print(matrix[:, 1])  # Output: [2 5] (second column)

Summary​

  • Use square brackets [] for indexing and slicing
  • 1D: arr[start:stop]
  • 2D: arr[row, col] or use : to slice rows/columns

What’s Next​

You’ll now practice accessing and slicing values from arrays in a Jupyter Notebook.