pandas replace zeros with previous non zero value

Question

I have the following dataframe:

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"?.

The output should look like:

[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 values but he was asking exclusively for a numpy solution)

Answer 1

You can use replace with 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

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)