pandas 熊猫用以前的非零值替换零
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pandas replace zeros with previous non zero value
提问by Gabriel
I have the following dataframe:
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'])
How can I fill the zeros with the previous non-zero value using pandas? Is there a fillna that is not just for "NaN"?.
如何使用Pandas用以前的非零值填充零？是否有不只是“NaN”的fillna？
The output should look like:
输出应如下所示：
[1, 1, 1, 2, 2, 4, 6, 8, 8, 8, 8, 8, 2, 1]
(This question was asked before here Fill zero values of 1d numpy array with last non-zero valuesbut he was asking exclusively for a numpy solution)
（这个问题在这里之前被问到用最后一个非零值填充 1d numpy 数组的零值，但他专门要求一个 numpy 解决方案）
回答by Zero
You can use replacewith method='ffill'
你可以用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
To get numpy array, work on values
要获得 numpy 数组，请继续 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)
回答by Abhay Bh
This is a better answer to the previous one, since the previous answer returns a dataframe which hides all zero values.
这是对前一个更好的答案，因为前一个答案返回一个隐藏所有零值的数据帧。
Instead, if you use the following line of code -
相反，如果您使用以下代码行 -
df['A'].mask(df['A'] == 0).ffill(downcast='infer')
Then this resolves the problem. It replaces all 0 values with previous values.
然后这解决了问题。它用以前的值替换所有 0 值。
