Python 从列表中删除 nan
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Removing a nan from a list
提问by ???? ?????
While trying to work on a project with pandas I have run into a problem. I had a list with a nanvalue in it and I couldn't remove it.
在尝试使用 Pandas 进行项目时，我遇到了一个问题。我有一个包含nan值的列表，但无法删除它。
I have tried:
我试过了：
incoms=data['int_income'].unique().tolist()
incoms.remove('nan')
But it didn't work:
但它没有用：
list.remove(x): x not in list"
list.remove(x): x 不在列表中”
The list incomsis as follows:
名单incoms如下：
[75000.0, 50000.0, 0.0, 200000.0, 100000.0, 25000.0, nan, 10000.0, 175000.0, 150000.0, 125000.0]
回答by jezrael
I think you need dropnafor remove NaNs:
我认为您需要dropna删除NaNs：
incoms=data['int_income'].dropna().unique().tolist()
print (incoms)
[75000.0, 50000.0, 0.0, 200000.0, 100000.0, 25000.0, 10000.0, 175000.0, 150000.0, 125000.0]
And if all values are integers only:
如果所有值都只是整数：
incoms=data['int_income'].dropna().astype(int).unique().tolist()
print (incoms)
[75000, 50000, 0, 200000, 100000, 25000, 10000, 175000, 150000, 125000]
Or remove NaNs by selecting all non NaN values by numpy.isnan:
或者NaN通过以下方式选择所有非 NaN 值来删除s numpy.isnan：
a = data['int_income'].unique()
incoms= a[~np.isnan(a)].tolist()
print (incoms)
[75000.0, 50000.0, 0.0, 200000.0, 100000.0, 25000.0, 10000.0, 175000.0, 150000.0, 125000.0]
a = data['int_income'].unique()
incoms= a[~np.isnan(a)].astype(int).tolist()
print (incoms)
[75000, 50000, 0, 200000, 100000, 25000, 10000, 175000, 150000, 125000]
Pure python solution - slowier if big DataFrame:
纯 python 解决方案 - 如果大则更慢DataFrame：
incoms=[x for x in list(set(data['int_income'])) if pd.notnull(x)]
print (incoms)
[0.0, 100000.0, 200000.0, 25000.0, 125000.0, 50000.0, 10000.0, 150000.0, 175000.0, 75000.0]
incoms=[int(x) for x in list(set(data['int_income'])) if pd.notnull(x)]
print (incoms)
[0, 100000, 200000, 25000, 125000, 50000, 10000, 150000, 175000, 75000]
回答by zoubida13
What you can do is simply get a cleaned list where you don't put the values that, once converted to strings, are 'nan'.
您可以做的只是获得一个清理过的列表，您不要在其中放置一旦转换为字符串的值为“nan”的值。
The code would be :
代码将是：
incoms = [incom for incom in incoms if str(incom) != 'nan']
回答by Rafael Valero
A possibility in that particular case is to remove nans earlier to avoid to do it in the list:
在这种特殊情况下的一种可能性是提前删除 nans 以避免在列表中执行它：
incoms=data['int_income'].dropna().unique().tolist()
