pandas 用先前的非零值替换零 [英] pandas replace zeros with previous non zero value

问题描述

我有以下数据框:

index = range(14)
data = [1, 0, 0, 2, 0, 4, 6, 8, 0, 0, 0, 0, 2, 1]
df = pd.DataFrame(data=data, index=index, columns = ['A'])

如何使用熊猫用先前的非零值填充零?是否有不仅仅用于"NaN"的fillna?

How can I fill the zeros with the previous non-zero value using pandas? Is there a fillna that is not just for "NaN"?.

输出应如下所示:

[1, 1, 1, 2, 2, 4, 6, 8, 8, 8, 8, 8, 2, 1]

(此问题之前在这里问过

(This question was asked before here Fill zero values of 1d numpy array with last non-zero values but he was asking exclusively for a numpy solution)

推荐答案

您可以将replace与method='ffill'

In [87]: df['A'].replace(to_replace=0, method='ffill')
Out[87]:
0     1
1     1
2     1
3     2
4     2
5     4
6     6
7     8
8     8
9     8
10    8
11    8
12    2
13    1
Name: A, dtype: int64

要获取numpy数组，请在values

To get numpy array, work on values

In [88]: df['A'].replace(to_replace=0, method='ffill').values
Out[88]: array([1, 1, 1, 2, 2, 4, 6, 8, 8, 8, 8, 8, 2, 1], dtype=int64)

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