## Flatten or Ravel? When to Choose the Right Numpy Function for Flattening Arrays?

Comparing Numpy's flatten and ravel Functions: Understanding the Copy vs. View Distinction

Despite producing similar flattened representations of multidimensional arrays, numpy's flatten and ravel functions exhibit significant differences in their operations.

Understanding the Output:

Consider the following example:

<code class="python">import numpy as np
y = np.array(((1, 2, 3), (4, 5, 6), (7, 8, 9)))
print(y.flatten())  # Output: [1 2 3 4 5 6 7 8 9]
print(y.ravel())  # Output: [1 2 3 4 5 6 7 8 9]</code>

As demonstrated, both functions yield the same flattened list.

Differences in Operation:

The distinction between flatten and ravel lies in how they handle the original array's data:

• flatten: Always returns a copy of the flattened array. Modifications to the returned array will not affect the original array.
• ravel: Returns a contiguous view of the original array whenever possible. If the array can be flattened without memory copying, a view is returned instead of a copy. However, modifying the returned array may propagate changes to the original array.

When to Use Which Function:

• Use flatten when: You need a copy of the flattened array and modifications to the returned array should not affect the original array.
• Use ravel when: You want to avoid memory copying and are willing to handle potential modifications to the original array.

In summary, flatten always returns a safe copy for independent modifications, while ravel returns a view when possible, maximizing performance at the potential risk of data contamination.

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