Pandas - 对 Na 以外的值使用“填充" [英] Pandas - Using 'ffill' on values other than Na

问题描述

有没有办法对不是 NaN 的值使用 ffill 方法?

Is there a way to use ffill method on values that are not NaN?

我的数据框中有 NaN，但我使用

I have NaN in my dataframe, but I have added these NaN using

addNan = sample['colA'].replace(['A'], 'NaN')

这就是我的 DataFrame，df 的样子

So this is what my DataFrame, df looks like

ColA  ColB  ColC  ColD
 B      A     A    C
 NaN    B     A    A
 C      D     D    A
 NaN    A     A    B

我正在尝试使用 ffill 填充这些 NaN ，因此它们由最后一个已知值填充.

And I'm trying to fill these NaN using ffill , so they are populated by the last known value.

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

这没什么区别，也试过 Na 而不是 NaN

This doesn't make a difference, also tried Na instead of NaN

推荐答案

我认为你需要先将 NaN 替换为 np.nan，因为 NaN只是文本:

I think you need first replace NaN to np.nan, because NaN is only text:

import pandas as pd
import numpy as np

print (sample)
  ColA ColB ColC ColD
0    B    A    A    C
1    A    B    A    A
2    C    D    D    A
3    A    A    A    B

sample['ColA'] = sample['ColA'].replace(['A'], np.nan)
print (sample)
  ColA ColB ColC ColD
0    B    A    A    C
1  NaN    B    A    A
2    C    D    D    A
3  NaN    A    A    B

如果使用inplace = True，则返回None，但就地填充值:

If use inplace = True, it return None, but inplace fill values:

sample.fillna(method='ffill', inplace = True)
#sample.ffill(inplace = True)
print (sample)
  ColA ColB ColC ColD
0    B    A    A    C
1    B    B    A    A
2    C    D    D    A
3    C    A    A    B

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