在 pandas 数据框中使用NaN查找行的整数索引 [英] Find integer index of rows with NaN in pandas dataframe

问题描述

我有一个这样的熊猫DataFrame:

I have a pandas DataFrame like this:

                    a         b
2011-01-01 00:00:00 1.883381  -0.416629
2011-01-01 01:00:00 0.149948  -1.782170
2011-01-01 02:00:00 -0.407604 0.314168
2011-01-01 03:00:00 1.452354  NaN
2011-01-01 04:00:00 -1.224869 -0.947457
2011-01-01 05:00:00 0.498326  0.070416
2011-01-01 06:00:00 0.401665  NaN
2011-01-01 07:00:00 -0.019766 0.533641
2011-01-01 08:00:00 -1.101303 -1.408561
2011-01-01 09:00:00 1.671795  -0.764629

是否存在一种有效的方法来查找具有NaN的行的整数"索引?在这种情况下，所需的输出应为[3, 6].

Is there an efficient way to find the "integer" index of rows with NaNs? In this case the desired output should be [3, 6].

推荐答案

对于DataFrame df:

For DataFrame df:

import numpy as np
index = df['b'].index[df['b'].apply(np.isnan)]

将带回您可以用来索引回df的MultiIndex，例如:

will give you back the MultiIndex that you can use to index back into df, e.g.:

df['a'].ix[index[0]]
>>> 1.452354

对于整数索引:

df_index = df.index.values.tolist()
[df_index.index(i) for i in index]
>>> [3, 6]

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