如何用pandas DataFrame中的上一个或下一个值替换NaN?

问题描述

假设我有一个带有某些 NaN s的DataFrame:

Suppose I have a DataFrame with some NaNs:

>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
>>> df
    0   1   2
0   1   2   3
1   4 NaN NaN
2 NaN NaN   9

我需要做的是将每个 NaN 替换为其上方同一列中的第一个非 NaN 值.假定第一行将永远不包含 NaN .因此，对于前面的示例，结果将是

What I need to do is replace every NaN with the first non-NaN value in the same column above it. It is assumed that the first row will never contain a NaN. So for the previous example the result would be

   0  1  2
0  1  2  3
1  4  2  3
2  4  2  9

我可以逐列，逐个元素地循环遍历整个DataFrame并直接设置值，但是是否有一种简单的方法(最佳无循环方法)来实现这一点?

I can just loop through the whole DataFrame column-by-column, element-by-element and set the values directly, but is there an easy (optimally a loop-free) way of achieving this?

推荐答案

您可以使用 fillna 方法，并将该方法指定为 ffill (正向填充):

You could use the fillna method on the DataFrame and specify the method as ffill (forward fill):

>>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
>>> df.fillna(method='ffill')
   0  1  2
0  1  2  3
1  4  2  3
2  4  2  9

此方法...

相反，还有一个 bfill 方法.

此方法不会就地修改DataFrame-您需要将返回的DataFrame重新绑定到变量，或者指定 inplace = True :

This method doesn't modify the DataFrame inplace - you'll need to rebind the returned DataFrame to a variable or else specify inplace=True:

df.fillna(method='ffill', inplace=True)

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