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Replacing missing values (NaNs) for certain columns in Pandas DataFrame

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Handling Missing Values
schedule Mar 10, 2022
Last updated
local_offer PythonPandas
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tocTable of Contents
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To replace NaN present in certain columns, use the DataFrame's fillna(~) 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

To fill NaN of columns A and C, provide a dict or Series like so:

df.fillna({"A":"@","C":10})
A B C
0 @ 7.0 9.0
1 5 NaN 10.0
2 6 8.0 10.0

Here, note the following:

  • NaNs in column A gets replaced by "@".

  • NaNs in column C gets replaced by 10.

robocat
Published by Isshin Inada
Edited by 0 others
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