ValueError: The truth value of a Series is ambiguous - python-2.7

Question

>>> df.head()
№ Summer Gold Silver Bronze Total № Winter \
Afghanistan (AFG) 13 0 0 2 2 0
Algeria (ALG) 12 5 2 8 15 3
Argentina (ARG) 23 18 24 28 70 18
Armenia (ARM) 5 1 2 9 12 6
Australasia (ANZ) [ANZ] 2 3 4 5 12 0
Gold.1 Silver.1 Bronze.1 Total.1 № Games Gold.2 \
Afghanistan (AFG) 0 0 0 0 13 0
Algeria (ALG) 0 0 0 0 15 5
Argentina (ARG) 0 0 0 0 41 18
Armenia (ARM) 0 0 0 0 11 1
Australasia (ANZ) [ANZ] 0 0 0 0 2 3
Silver.2 Bronze.2 Combined total
Afghanistan (AFG) 0 2 2
Algeria (ALG) 2 8 15
Argentina (ARG) 24 28 70
Armenia (ARM) 2 9 12
Australasia (ANZ) [ANZ] 4 5 12
Not sure why do I see this error:
>>> df['Gold'] > 0 | df['Gold.1'] > 0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/ankuragarwal/data_insight/env/lib/python2.7/site-packages/pandas/core/generic.py", line 917, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Whats ambiguous here ?
But this works:
>>> (df['Gold'] > 0) | (df['Gold.1'] > 0)

Answer 1

Assuming we have the following DF:
In [35]: df
Out[35]:
a b c
0 9 0 1
1 7 7 4
2 1 8 9
3 6 7 5
4 1 4 6
The following command:
df.a > 5 | df.b > 5
because | has higher precedence (compared to >) as it's specified in the Operator precedence table) it will be translated to:
df.a > (5 | df.b) > 5
which will be translated to:
df.a > (5 | df.b) and (5 | df.b) > 5
step by step:
In [36]: x = (5 | df.b)
In [37]: x
Out[37]:
0 5
1 7
2 13
3 7
4 5
Name: c, dtype: int32
In [38]: df.a > x
Out[38]:
0 True
1 False
2 False
3 False
4 False
dtype: bool
In [39]: x > 5
Out[39]:
0 False
1 True
2 True
3 True
4 False
Name: b, dtype: bool
but the last operation won't work:
In [40]: (df.a > x) and (x > 5)
---------------------------------------------------------------------------
...
skipped
...
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
The error message above might lead inexperienced users to do something like this:
In [12]: (df.a > 5).all() | (df.b > 5).all()
Out[12]: False
In [13]: df[(df.a > 5).all() | (df.b > 5).all()]
...
skipped
...
KeyError: False
But in this case you just need to set your precedence explicitly in order to get expected result:
In [10]: (df.a > 5) | (df.b > 5)
Out[10]:
0 True
1 True
2 True
3 True
4 False
dtype: bool
In [11]: df[(df.a > 5) | (df.b > 5)]
Out[11]:
a b c
0 9 0 1
1 7 7 4
2 1 8 9
3 6 7 5

Answer 2

This is the real reason for the error:
http://pandas.pydata.org/pandas-docs/stable/gotchas.html
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