Why does .copy() fail to make a deep copy when the elements of the original dataframe are lists? [duplicate]

Question

tl;dr: Operations on a dataframe create using .copy() do not affect the original dataframe when its elements are of a certain type, but they do when the elements are list.

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Cases where Pandas behaves as expected

Creating the new dataframe by assignment

I have understood that under some conditions, creating a new variable foo using an existing pandas dataframe bar can mean that operations on foo creates changes in bar as well. Hence:

foo = pd.DataFrame({'A':['one','two','three'],'B':['apple','banana','grape'],'C':['cat','zebra','donkey']})  
bar = foo
bar['A'] = ['four','five','six']

will give foo['A'] = ['four','five','six'].

Using .copy() and performing operations on columns

Fair enough. We spend no more than a minute searching to find that what you really need to do is:

foo = pd.DataFrame({'A':['one','two','three'],'B':['apple','banana','grape'],'C':['cat','zebra','donkey']})
bar = foo.copy()   
bar['A'] = ['four','five','six']

and faithfully enough, this gives foo['A'] = ['one','two','three'].

Using .copy() and performing operations on single elements

Likewise, performing an operation on just one element in foo leaves bar unaffected as desired:

foo = pd.DataFrame({'A':['one','two','three'],'B':['apple','banana','grape'],'C':['cat','zebra','donkey']})
bar = foo.copy()   
bar['A'][0] = 'four'

which as expected/desired gives foo['A']=['one','two','three'].

Where things go wrong: Using .copy() and forming operations on single elements that are lists

Here comes the twist:

foo = pd.DataFrame({'A':[['one','two','three'],['four','five','six'],['seven','eight','nine']]})
bar = foo.copy()   
bar['A'][0][0] = 'apple'

gives foo['A'][0] = ['apple','two','three'].

Why does this happen? What is the correct way to make a copy of the original dataframe if I don't want operations on the new dataframe to affect the original dataframe when the elements of the dataframe are lists?