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Replace Missing Values with Interpolation

Another approach to deal with numerical data is using interpolation. Each NaN value will be replaced with the result of interpolation between the previous and the next entry over the column. Let's apply the interpolate() function to numeric column Age by setting the limit direction to forward. This means that linear interpolation is applied from the first line to the last.


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data = data.interpolate(method = 'linear', limit_direction = 'forward')

Task

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Fill the empty places in the code. Compare the data in Age column before and after using interpolation (look at the last 10 rows).

Solution

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Section 2. Chapter 6

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Replace Missing Values with Interpolation


              1
            
data = data.interpolate(method = 'linear', limit_direction = 'forward')

Task

Swipe to start coding

Fill the empty places in the code. Compare the data in Age column before and after using interpolation (look at the last 10 rows).

Solution

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below