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Using interpolation to fill missing values (NaNs) in Pandas DataFrame

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Handling Missing Values
schedule Jul 1, 2022
Last updated
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To fill missing values using interpolation, use the DataFrame's interpolate(~) method.

NOTE

The method interpolate(~) has 8 parameters to tweak. Click here for the full documentation.

Example

Consider the following DataFrame with some missing values:

df = pd.DataFrame({"A":[3,np.nan,5,6],"B":[1,5,np.nan,9],"C":[1,5,np.nan,np.nan]})
df
A B C
0 3.0 1.0 1.0
1 NaN 5.0 5.0
2 5.0 NaN NaN
3 6.0 9.0 NaN

To fill NaN using linear interpolation:

df.interpolate() # method="linear"
A B C
0 3.0 1.0 1.0
1 4.0 5.0 5.0
2 5.0 7.0 5.0
3 6.0 9.0 5.0

Notice how the two NaN in column C were filled using forward-fill (default) instead since linear interpolation cannot be performed without an upper bound.

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Published by Isshin Inada
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