计算DataFrame各列的第非缺失值

我有一个看起来像这样的DataFrame:

I have a DataFrame which looks like this:

            1125400  5430095  1095751
2013-05-22   105.24      NaN  6507.58
2013-05-23   104.63      NaN  6393.86
2013-05-26   104.62      NaN  6521.54
2013-05-27   104.62      NaN  6609.31
2013-05-28   104.54    87.79  6640.24
2013-05-29   103.91    86.88  6577.39
2013-05-30   103.43    87.66  6516.55
2013-06-02   103.56    87.55  6559.43

我想计算每列中的第一个非NaN值.

I would like to compute the first non-NaN value in each column.

作为>查找第一个和最后一个非NaN熊猫数据框中的值指出，可以使用first_valid_index.不幸的是，它返回第一行，其中至少一个元素不是NaN，并且每个列都不起作用.

As Locate first and last non NaN values in a Pandas DataFrame points out, first_valid_index can be used. Unfortunately, it returns the first row where at least one element is not NaN and does not work per-column.