Size immutability in pandas data structure

While going through pandas Documentation for version 0.24.1 here, I came across this statement. "All pandas data structures are value-mutable (the values they contain can be altered) but not always size-mutable. The length of a Series cannot be changed, but, for example, columns can be inserted into a DataFrame."

import pandas as pd
test_s = pd.Series([1,2,3])
id(test_s) # output: 140485359734400 (will vary)
len(test_s) # output: 3
test_s[3] = 37
id(test_s) # output: 140485359734400
len(test_s) # output: 4

The meaning of size immutable as per my inference is that operations like appending and deleting an element are not allowed, which is clearly not the case. Even the identity of the object remains the same, ruling out the possibility of a new object creation with the same name. So, what does size immutability actually mean?

Solution 1:

Appending and deleting are allowed, but that doesn't necessarily imply the Series is mutable.

Series/DataFrames are internally represented by NumPy arrays which are immutable (fixed size) to allow a more compact memory representation and better performance.

When you assign to a Series, you're actually calling Series.__setitem__ (which then delegates to NDFrame.__loc__) which creates a new array. This new array is then assigned back to the same Series (of course, as the end user, you don't get to see this), giving you the illusion of mutability.