1.1 Using If/Truth Statements with pandas

pandas follows the numpy convention of raising an error when you try to convert something to a bool. This happens in a if or when using the boolean operations, and, or, or not. It is not clear what the result of

>>> if pd.Series([False, True, False]):
     ...

should be. Should it be True because it’s not zero-length? False because there are False values? It is unclear, so instead, pandas raises a ValueError:

>>> if pd.Series([False, True, False]):
    print("I was true")
Traceback
    ...
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().

If you see that, you need to explicitly choose what you want to do with it (e.g., use any(), all() or empty). or, you might want to compare if the pandas object is None

>>> if pd.Series([False, True, False]) is not None:
       print("I was not None")
>>> I was not None

or return if any value is True.

>>> if pd.Series([False, True, False]).any():
       print("I am any")
>>> I am any

To evaluate single-element pandas objects in a boolean context, use the method .bool():

In [1]: pd.Series([True]).bool()
Out[1]: True

In [2]: pd.Series([False]).bool()
Out[2]: False

In [3]: pd.DataFrame([[True]]).bool()
Out[3]: True

In [4]: pd.DataFrame([[False]]).bool()
Out[4]: False

>>> s = pd.Series(range(5))
>>> s == 4
0    False
1    False
2    False
3    False
4     True
dtype: bool