如果 pandas dataframe.loc 位置不存在，则返回默认值
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return default if pandas dataframe.loc location doesn't exist
提问by fantabolous
I find myself often having to check whether a column or row exists in a dataframe before trying to reference it. For example I end up adding a lot of code like:
我发现自己在尝试引用之前经常必须检查数据框中是否存在列或行。例如，我最终添加了很多代码，例如：
if 'mycol' in df.columns and 'myindex' in df.index: x = df.loc[myindex, mycol]
else: x = mydefault
Is there any way to do this more nicely? For example on an arbitrary object I can do x = getattr(anobject, 'id', default)- is there anything similar to this in pandas? Really any way to achieve what I'm doing more gracefully?
有没有办法更好地做到这一点？例如，在我可以做的任意对象上x = getattr(anobject, 'id', default)- 在Pandas中是否有类似的东西？真的有什么方法可以更优雅地实现我正在做的事情吗？
回答by EdChum
There is a method for Series:
有一种方法Series：
So you could do:
所以你可以这样做：
df.mycol.get(myIndex, NaN)
Example:
例子：
In [117]:
df = pd.DataFrame({'mycol':arange(5), 'dummy':arange(5)})
df
Out[117]:
dummy mycol
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
[5 rows x 2 columns]
In [118]:
print(df.mycol.get(2, NaN))
print(df.mycol.get(5, NaN))
2
nan
回答by FooBar
Python has this mentality to ask for forgiveness instead of permission. You'll find a lot of posts on this matter, such as this one.
Python 有这种请求宽恕而不是许可的心态。你会发现很多关于这个问题的帖子，比如这个。
In Python catching exceptions is relatively inexpensive, so you're encouraged to use it. This is called the EAFPapproach.
在 Python 中捕获异常的成本相对较低，因此鼓励您使用它。这称为EAFP方法。
For example:
例如：
try:
x = df.loc['myindex', 'mycol']
except KeyError:
x = mydefault
