CODEWITHSUNDEEP
pythonpandas
Gabriel
Gabriel

Reputation: 3797

pandas replace zeros with previous non zero value

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)

Upvotes: 37

Views: 65968

Answers (3)

HarryChil
HarryChil

Reputation: 559

The correct way to do this now is:

df.replace(0, np.nan).ffill()

df.replace(to_replace=0, method='ffill') : this was deprecated in Pandas version 2.1.0.

Upvotes: 6

Abhay Bh
Abhay Bh

Reputation: 132

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.

Upvotes: -1

Zero
Zero

Reputation: 76927

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)

Upvotes: 66

Related Questions