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Replacing values with NaNs in Pandas DataFrame

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Handling Missing Values
schedule Mar 9, 2022
Last updated
local_offer PythonPandas
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To replace values with NaN, use the DataFrame's replace(~) method.

Replacing value with NaN

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,"NONE"]})
df
A
0 3
1 NONE

To replace "NONE" values with NaN:

import numpy as np
df.replace("NONE", np.nan)
A
0 3.0
1 NaN

Note that the replacement is not done in-place, that is, a new DataFrame is returned and the original df is kept intact. To perform the replacement in-place, set inplace=True.

Replacing multiple values with NaN

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,5],"B":[4,6]}, index=["a","b"])
df
A B
a 3 4
b 5 6

To replace values 3 and 4 with np.nan:

df.replace([3,4], np.nan)
A B
a NaN NaN
b 5.0 6.0

Conditionally replacing values with NaN

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,5],"B":[4,6]}, index=["a","b"])
df
A B
a 3 4
b 5 6

To replace values larger than 4 with NaN:

df[df > 4] = np.nan
df
A B
a 3.0 4.0
b NaN NaN
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Published by Isshin Inada
Edited by 0 others
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