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.
1data = data.interpolate(method = 'linear', limit_direction = 'forward')
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
Thanks for your feedback!
single
Ask AI
Ask AI
Ask anything or try one of the suggested questions to begin our chat
Summarize this chapter
Explain the code in file
Explain why file doesn't solve the task
Awesome!
Completion rate improved to 5.56
Replace Missing Values with Interpolation
Swipe to show menu
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.
1data = data.interpolate(method = 'linear', limit_direction = 'forward')
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
Thanks for your feedback!
Awesome!
Completion rate improved to 5.56single