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Lære Replace Missing Values with Interpolation | Data Cleaning
Preprocessing Data

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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.

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

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).

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
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Seksjon 2. Kapittel 6
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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.

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

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).

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

close

Awesome!

Completion rate improved to 5.56

Sveip for å vise menyen

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