Garbage collection and array references in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What happens to a NumPy array when all references to it are deleted?

The array's memory is freed by Python's garbage collector because no references remain to it.

beginner

How does assigning one NumPy array to another variable affect memory?

Both variables point to the same array in memory; no new array is created unless explicitly copied.

beginner

What method can you use to create a separate copy of a NumPy array to avoid shared references?

Use the copy() method, e.g., new_array = old_array.copy().

intermediate

Why is it important to understand references when working with NumPy arrays?

Because modifying one variable can change the array seen by another variable if they share the same reference.

intermediate

How does Python's garbage collector know when to free a NumPy array's memory?

It tracks reference counts; when the count drops to zero, the memory is released.

If you assign b = a where a is a NumPy array, what happens?

A <code>b</code> points to the same array as <code>a</code>.

B A new array is created and stored in <code>b</code>.

C The array is deleted from memory.

D Python raises an error.

Assigning b = a makes b reference the same array as a. No new array is created.

Which method creates a new independent copy of a NumPy array?

A <code>array.duplicate()</code>

B <code>array.assign()</code>

C <code>array.clone()</code>

D <code>array.copy()</code>

The copy() method creates a new array with its own memory.

What triggers Python's garbage collector to free a NumPy array's memory?

A When the array is explicitly deleted with <code>del</code>.

B When no references to the array remain.

C When the array is larger than 1GB.

D When the program ends.

Memory is freed when the reference count drops to zero, meaning no variables point to the array.

If you modify a NumPy array through one variable, what happens to other variables referencing the same array?

A They see the updated changes.

B They keep the old values.

C They raise an error.

D They become None.

Since they reference the same array, changes are visible through all variables.

Which of the following is NOT true about NumPy array references?

A Multiple variables can point to the same array.

B Using <code>copy()</code> creates a new array in memory.

C Assigning one array to another variable copies the data automatically.

D Garbage collection frees arrays with zero references.

Assigning one variable to another does NOT copy data; it just creates a new reference.

Explain how Python's garbage collection works with NumPy arrays and why references matter.

Think about how Python tracks variables pointing to the same array.

Describe the difference between assigning a NumPy array to a new variable and creating a copy of it.

Consider what happens in memory and when you change the array.