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NumPy | shares_memory method

schedule Aug 12, 2023
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Numpy's `shares_memory(~)` method returns True if the two input arrays share the same memory.

Parameters

1. `a` | `array-like`

The first input array.

2. `b` | `array-like`

The second input array.

3. `max_work` | `int` | `optional`

The maximum number of checks to perform when comparing memory location:

max_work

Meaning

MAY_SHARE_EXACT

The inner contents of the array are checked. True is returned only if there is at least a pair of elements that shares the same memory.

MAY_SHARE_BOUNDS

Only the two bounds occupied by the arrays are checked.

By default, `max_work=MAY_SHARE_EXACT`.

Return value

A single boolean where True means that the two input arrays share the memory.

Examples

``` a = np.array([1,2])b = np.array([1,2])np.shares_memory(a,b) False `````` a = np.array([1,2])b = anp.shares_memory(a,b) True ```
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