如何从Pandas DataFrame中创建列表列表，跳过Nan值

问题描述

我有一个看起来像大熊猫的数据框

I have a pandas dataframe that looks roughly like

    foo   foo2   foo3  foo4
a   NY    WA     AZ    NaN
b   DC    NaN    NaN   NaN
c   MA    CA     NaN   NaN

我想对此数据帧的观察结果进行嵌套列表，但省略NaN值，所以我有类似[['NY'，'WA'，'AZ']，['DC']的内容，['MA'，CA'].

I'd like to make a nested list of the observations of this dataframe, but omit the NaN values, so I have something like [['NY','WA','AZ'],['DC'],['MA',CA'].

此数据帧中有一个模式，如果有区别，那么如果fooX为空，则后续列fooY也将为空.

There is a pattern in this dataframe, if that makes a difference, such that if fooX is empty, the subsequent column fooY will also be empty.

我最初在下面有类似以下代码的内容.我敢肯定有更好的方法可以做到这一点

I originally had something like this code below. I'm sure there's a nicer way to do this

A = [[i] for i in subset_label['label'].tolist()]
B = [i for i in subset_label['label2'].tolist()]
C = [i for i in subset_label['label3'].tolist()]
D = [i for i in subset_label['label4'].tolist()]
out_list = []
for index, row in subset_label.iterrows():
out_list.append([row.label, row.label2, row.label3, row.label4])
out_list

推荐答案

尝试一下:

In [77]: df.T.apply(lambda x: x.dropna().tolist()).tolist()
Out[77]: [['NY', 'WA', 'AZ'], ['DC'], ['MA', 'CA']]

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