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Replacing all NaN values with zeros in a Pandas DataFrame

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Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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To replace all NaN values with zeros in a Pandas DataFrame, use the fillna(~) method.

Example - filling all columns of a DataFrame

Consider the following DataFrame with some NaN values:

df = pd.DataFrame({"A":[np.nan,3],"B":[4,np.nan]})
df
   A    B
0  NaN  4.0
1  3.0  NaN

To convert all NaN to zeros:

df.fillna(0)
   A    B
0  0.0  4.0
1  3.0  0.0

Example - filling some columns of a DataFrame

Consider the same DataFrame as above:

df
   A    B
0  NaN  4.0
1  3.0  NaN

To convert NaN in column A to zeros:

df["A"] = df["A"].fillna(0)
df
A B
0 0.0 4.0
1 3.0 NaN
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Published by Isshin Inada
Edited by 0 others
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