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

schedule Aug 12, 2023
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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 C0 3.0 1.0 1.01 NaN 5.0 5.02 5.0 NaN NaN3 6.0 9.0 NaN ```

To fill `NaN` using linear interpolation:

``` df.interpolate() # method="linear" A B C0 3.0 1.0 1.01 4.0 5.0 5.02 5.0 7.0 5.03 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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