如果列表中有NoneType和pandas._lib .missing. list。NAType对象比使用:

[i for i in lst if pd.notnull(i)]

>>> L = [0, 23, 234, 89, None, 0, 35, 9]
>>> [x for x in L if x is not None]
[0, 23, 234, 89, 0, 35, 9]

只是为了好玩，这里介绍了如何在不使用lambda的情况下调整过滤器来实现这一点，(我不建议使用此代码-仅用于科学目的)

>>> from operator import is_not
>>> from functools import partial
>>> L = [0, 23, 234, 89, None, 0, 35, 9]
>>> list(filter(partial(is_not, None), L))
[0, 23, 234, 89, 0, 35, 9]

列表理解可能是最干净的方式:

>>> L = [0, 23, 234, 89, None, 0, 35, 9
>>> [x for x in L if x is not None]
[0, 23, 234, 89, 0, 35, 9]

还有一种函数式编程方法，但它更复杂:

>>> from operator import is_not
>>> from functools import partial
>>> L = [0, 23, 234, 89, None, 0, 35, 9]
>>> list(filter(partial(is_not, None), L))
[0, 23, 234, 89, 0, 35, 9]

如果列表中有NoneType和pandas._lib .missing. list。NAType对象比使用:

[i for i in lst if pd.notnull(i)]

@jamylak的回答非常好，但是如果你不想导入几个模块来完成这个简单的任务，就在原地写你自己的lambda:

>>> L = [0, 23, 234, 89, None, 0, 35, 9]
>>> filter(lambda v: v is not None, L)
[0, 23, 234, 89, 0, 35, 9]

如果这都是列表的列表，你可以修改sir @Raymond的答案

L = [[None]， [123]， [None]， [151]] no_none_val = list(filter(无。__ne__， [x[0] for x in L])) 然而对于python2

no_none_val = [x[0] for x in L if x[0] not None] """ Both returns [123, 151]""" "

如果变量不是List中的变量，<< list_index[0]无>>