pandas views vs copy : the docs says "nobody knows"?

Question

There's lots of questions on StackOverflow about chained indexing and whether a particular operation makes a view or a copy. (for instance, here or here). I still don't fully get it, but the amazing part is the official docs say "nobody knows". (!?!??) Here's an example from the docs; can you tell me if they really meant that, or if they're just being flippant?

From http://pandas-docs.github.io/pandas-docs-travis/indexing.html?highlight=view#why-does-assignment-fail-when-using-chained-indexing

def do_something(df):
   foo = df[['bar', 'baz']]  # Is foo a view? A copy? Nobody knows!
   # ... many lines here ...
   foo['quux'] = value       # We don't know whether this will modify df or not!
   return foo

Seriously? For that specific example, is it really true that "nobody knows" and this is non-deterministic? Will that really behave differently on two different dataframes? The rules are really that complex? Or did the guy mean there is a definite answer but just that most people aren't aware of it?

Answer 1

I think I can demonstrate something to clarify your situation, in your example, initially it will be a view but once you try to modify by adding a column it turns into a copy. You can test this by looking at the attribute ._is_view:

In [29]:
df = pd.DataFrame(np.random.randn(5,3), columns=list('abc'))
def doSomething(df):
    a = df[['b','c']]
    print('before ', a._is_view)
    a['d'] = 0
    print('after ', a._is_view)

doSomething(df)
df

before  True
after  False
Out[29]:
          a         b         c
0  0.108790  0.580745  1.820328
1  1.066503 -0.238707 -0.655881
2 -1.320731  2.038194 -0.894984
3 -0.962753 -3.961181  0.109476
4 -1.887774  0.909539  1.318677

So here we can see that initially a is a view on the original subsection of the original df, but once you add a column to this, this is no longer true and we can see that the original df is not modified.