在python字典中删除带有nan值的条目 [英] remove entries with nan values in python dictionary

问题描述

我有傻瓜. python中的字典:

I have the foll. dictionary in python:

OrderedDict([(30, ('A1', 55.0)), (31, ('A2', 125.0)), (32, ('A3', 180.0)), (43, ('A4', nan))])

是否可以删除任何值为NaN的条目?我试过了:

Is there a way to remove the entries where any of the values is NaN? I tried this:

{k: dict_cg[k] for k in dict_cg.values() if not np.isnan(k)}

如果soln同时适用于python 2和python 3，那就太好了

It would be great if the soln works for both python 2 and python 3

推荐答案

由于您拥有熊猫，因此您可以在此处利用熊猫的pd.Series.notnull函数，该函数可用于混合dtypes.

Since you have pandas, you can leverage pandas' pd.Series.notnull function here, which works with mixed dtypes.

>>> import pandas as pd
>>> {k: v for k, v in dict_cg.items() if pd.Series(v).notna().all()}
{30: ('A1', 55.0), 31: ('A2', 125.0), 32: ('A3', 180.0)}

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这不是答案的一部分，但可以帮助您了解我是如何找到解决方案的.尝试直接使用pd.notnull尝试解决此问题时，我遇到了一些奇怪的行为.

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This is not part of the answer, but may help you understand how I've arrived at the solution. I came across some weird behaviour when trying to solve this question, using pd.notnull directly.

以dict_cg[43].

>>> dict_cg[43]
('A4', nan)

pd.notnull不起作用.

>>> pd.notnull(dict_cg[43])
True

它将元组视为单个值(而不是值的可迭代).此外，将其转换为列表然后进行测试也会给出错误的答案.

It treats the tuple as a single value (rather than an iterable of values). Furthermore, converting this to a list and then testing also gives an incorrect answer.

>>> pd.notnull(list(dict_cg[43]))
array([ True,  True])

由于第二个值是nan，所以我要查找的结果应该是[True, False].预转换为系列后，它终于可以工作了:

Since the second value is nan, the result I'm looking for should be [True, False]. It finally works when you pre-convert to a Series:

>>> pd.Series(dict_cg[43]).notnull() 
0     True
1    False
dtype: bool

因此，解决方案是对其进行序列化，然后测试值.

So, the solution is to Series-ify it and then test the values.

沿着相似的线，另一种(公认的环形交叉路)解决方案是将其预转换为object dtype numpy数组，并且pd.notnull将直接起作用:

Along similar lines, another (admittedly roundabout) solution is to pre-convert to an object dtype numpy array, and pd.notnull will work directly:

>>> pd.notnull(np.array(dict_cg[43], dtype=object))
Out[151]: array([True,  False])

我想像pd.notnull直接将dict_cg[43]转换为幕后的字符串数组，将NaN呈现为字符串"nan"，因此不再是"null"值.

I imagine that pd.notnull directly converts dict_cg[43] to a string array under the covers, rendering the NaN as a string "nan", so it is no longer a "null" value.

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