ValueError:系列的真值不明确 [英] ValueError: The truth value of a Series is ambiguous
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问题描述
>>> 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
不确定为什么我会看到此错误:
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().
这里有什么歧义?
但这可行:
>>> (df['Gold'] > 0) | (df['Gold.1'] > 0)
推荐答案
假设我们有以下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
以下命令:
df.a > 5 | df.b > 5
因为|
具有更高的优先级(与>
相比),因此在运算符优先级表),它将被转换为:
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
将被翻译为:
df.a > (5 | df.b) and (5 | df.b) > 5
逐步:
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
但最后一次操作不起作用:
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().
上面的错误消息可能会导致缺乏经验的用户执行以下操作:
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
但是在这种情况下,您只需要显式设置优先级即可获得预期结果:
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
这篇关于ValueError:系列的真值不明确的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
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