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Replacing missing values (NaNs) with preceding values in Pandas DataFrame

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Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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To replace NaN with preceding values, use the DataFrame's fillna(method="ffill") method.

Examples

Consider the following DataFrame:

df = pd.DataFrame({"A":[None,5,6],"B":[7,None,8],"C":[9,None,None]})
df
A B C
0 NaN 7.0 9.0
1 5.0 NaN NaN
2 6.0 8.0 NaN

Replace NaN with preceding values in column

To fill NaN with the previous non-NaN value in the same column:

df.fillna(method="ffill") # or method="pad"
A B C
0 NaN 7.0 9.0
1 5.0 7.0 9.0
2 6.0 8.0 9.0

Notice how we still have a NaN on the top-left. This is because there is no preceding value for that entry in that column.

Replace NaN with preceding values in row

To fill NaN with the previous non-NaN value in the same row, set axis=1 like so:

df.fillna(method="ffill", axis=1)
A B C
0 NaN 7.0 9.0
1 5.0 5.0 5.0
2 6.0 8.0 8.0

Again, we end up with NaN on the top-left since it does not have any preceding value in that row.

NOTE

Click here for our full documentation on fillna(~).

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Published by Isshin Inada
Edited by 0 others
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