In my code, I have several variables which can either contain a pandas DataFrame or nothing at all. Let's say I want to test and see if a certain DataFrame has been created yet or not. My first thought would be to test for it like this:

if df1:
    # do something

However, that code fails in this way:

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Fair enough. Ideally, I would like to have a presence test that works for either a DataFrame or Python None.

Here is one way this can work:

if not isinstance(df1, type(None)):
    # do something

However, testing for type is really slow.

t = timeit.Timer('if None: pass')
t.timeit()
# approximately 0.04
t = timeit.Timer('if isinstance(x, type(None)): pass', setup='x=None')
t.timeit()
# approximately 0.4

Ouch. Along with being slow, testing for NoneType isn't very flexible, either.

A different solution would be to initialize df1 as an empty DataFrame, so that the type would be the same in both the null and non-null cases. I could then just test using len(), or any(), or something like that. Making an empty DataFrame seems kind of silly and wasteful, though.

Another solution would be to have an indicator variable: df1_exists, which is set to False until df1 is created. Then, instead of testing df1, I would be testing df1_exists. But this doesn't seem all that elegant, either.

Is there a better, more Pythonic way of handling this issue? Am I missing something, or is this just an awkward side effect all the awesome things about pandas?

Option 1 (my preferred option)

This is @Ami Tavory's

Please select his answer if you like this approach

It is very idiomatic python to initialize a variable with None then check for None prior to doing something with that variable.

df1 = None

if df1 is not None:
    print df1.head()

---

Option 2

However, setting up an empty dataframe isn't at all a bad idea.

df1 = pd.DataFrame()

if not df1.empty:
    print df1.head()

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Option 3

Just try it.

try:
    print df1.head()
# catch when df1 is None
except AttributeError:
    pass
# catch when it hasn't even been defined
except NameError:
    pass

---

Timing

When df1 is in initialized state or doesn't exist at all

When df1 is a dataframe with something in it

df1 = pd.DataFrame(np.arange(25).reshape(5, 5), list('ABCDE'), list('abcde'))
df1