What are some advanced slicing techniques in NumPy?

What are some advanced slicing techniques in NumPy?

Advanced slicing in NumPy allows you to extract specific elements, subarrays, or even modify arrays efficiently. For example, you can use slicing with steps, boolean indexing, or even fancy indexing.

Can you give me an example of slicing with steps?

Sure! If you have an array arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), you can slice it with steps like this: arr[::2] which will give you [0, 2, 4, 6, 8].

What about boolean indexing?

Boolean indexing allows you to select elements based on conditions. For example, arr[arr > 5] will return elements greater than 5, resulting in [6, 7, 8, 9].

Can you explain fancy indexing?

Fancy indexing lets you access multiple array elements using an array of indices. For instance, arr[[1, 3, 5]] will return the elements at indices 1, 3, and 5, which are [1, 3, 5].