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\         


        

Answer 1

This is a better answer to the previous one, since the previous answer returns a dataframe which hides all zero values.

Instead, if you use the following line of code -

df['A'].mask(df['A'] == 0).ffill(downcast='infer')

Then this resolves the problem. It replaces all 0 values with previous values.

Answer 2

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)