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Checking if a NumPy array is a view or copy

schedule Aug 10, 2023
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To check if a NumPy array is a view or not use the `base` property. To check if a NumPy array is a copy, we can use `np.shares_memory(~)` to check whether the two objects share memory or not.

Examples

View or not

Consider the following arrays `a` and `b`:

``` a = np.array([1,2])b = a.view() ```

To check if `b` is a view of `a`:

``` b.base is a True ```

Here, array `b` shares the same memory address as `a`, so `b.base` is `a` evaluates to `True`.

Share memory or not

Consider the following arrays `a` and `b`:

``` a = np.array([1,2])b = a.copy() ```

To check if `b` shares memory with `a`:

``` np.shares_memory(b, a) False ```

Given the two arrays do not share memory, we can say that `b` is a copy of `a`.

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