Pandas 缺失值：填充最接近的非 NaN 值
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Pandas missing values : fill with the closest non NaN value
提问by Clément F
Assume I have a pandas series with several consecutive NaNs. I know fillnahas several methods to fill missing values (backfilland fill forward), but I want to fill them with the closest non NaN value. Here's an example of what I have:
假设我有一个包含多个连续 NaN 的 Pandas 系列。我知道fillna有几种方法可以填充缺失值（backfill和fill forward），但我想用最接近的非 NaN 值填充它们。这是我所拥有的一个例子：
`s = pd.Series([0, 1, np.nan, np.nan, np.nan, np.nan, 3])`
And an example of what I want:
s = pd.Series([0, 1, 1, 1, 3, 3, 3])
和我想要的一个例子：
s = pd.Series([0, 1, 1, 1, 3, 3, 3])
Does anyone know I could do that?
有谁知道我能做到吗？
Thanks!
谢谢！
回答by DSM
You could use Series.interpolatewith method='nearest':
你可以使用Series.interpolate同method='nearest'：
In [11]: s = pd.Series([0, 1, np.nan, np.nan, np.nan, np.nan, 3])
In [12]: s.interpolate(method='nearest')
Out[12]:
0 0.0
1 1.0
2 1.0
3 1.0
4 3.0
5 3.0
6 3.0
dtype: float64
In [13]: s = pd.Series([0, 1, np.nan, np.nan, 2, np.nan, np.nan, 3])
In [14]: s.interpolate(method='nearest')
Out[14]:
0 0.0
1 1.0
2 1.0
3 2.0
4 2.0
5 2.0
6 3.0
7 3.0
dtype: float64
