np.take() and np.put() for advanced selection in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.take() do in NumPy?

np.take() selects elements from an array at specified indices. It is like picking items from a list using a list of positions.

beginner

How does np.put() work in NumPy?

np.put() replaces elements in an array at given indices with new values. Think of it as putting new items into specific spots in a list.

intermediate

What is the difference between np.take() and simple indexing like arr[indices]?

np.take() can handle multi-dimensional arrays with an axis argument, allowing selection along a specific axis. Simple indexing selects elements but may not support advanced axis selection.

intermediate

Can np.put() modify elements along a specific axis?

No, np.put() works on the flattened version of the array by default. To modify along an axis, you need to reshape or use other methods.

advanced

What happens if you use np.put() with indices outside the array size?

Indices are taken modulo the array size, so they wrap around. This means out-of-range indices loop back to valid positions.

What does np.take(arr, indices) return?

A Elements from <code>arr</code> at positions in <code>indices</code>

B A new array with elements replaced at <code>indices</code>

C The sum of elements at <code>indices</code>

D The shape of <code>arr</code>

np.take() picks elements from the array at the specified indices.

Which function would you use to replace elements in an array at specific positions?

A np.take()

B np.put()

C np.sum()

D np.reshape()

np.put() replaces elements at given indices.

What does np.put(arr, [0, 2], [10, 20]) do?

A Selects elements at positions 0 and 2

B Deletes elements at positions 0 and 2

C Returns the sum of elements at positions 0 and 2

D Replaces elements at positions 0 and 2 with 10 and 20

np.put() replaces elements at specified indices with new values.

If you want to select elements along a specific axis, which function is better?

A np.put()

B np.flatten()

C np.take() with axis argument

D np.sum()

np.take() supports an axis argument for selection along that axis.

What happens if np.put() receives an index larger than the array size?

A Wraps around using modulo

B Raises an error

C Ignores the index

D Deletes the last element

Indices wrap around using modulo the array size.

Explain how np.take() can be used to select elements from a 2D array along a specific axis.

Think about how you pick rows or columns by specifying the axis.

Describe the behavior of np.put() when given indices and values, including what happens with out-of-range indices.

Consider how the function modifies the array and handles indices beyond its size.