Handling missing data with interpolation
Interpolation is another widely used technique for imputing missing values, particularly in time series datasets. The pandas library provides the DataFrame.interpolate() method, offering flexible and powerful options for univariate imputation.For instance, linear interpolation imputes missing data by drawing a straight line between the two points surrounding the missing value. In a time series context, this means the missing value is estimated as a linear line based on the prior (past) value and the next (future) value. On the other hand, polynomial interpolation uses a curved line to estimate missing values between two points, enabling it to capture a non-linear relationship between points.Examples of some of the interpolation methods available in pandas include the following:
- Linear: Straight-line interpolation between points
- Nearest: Uses the nearest available value for imputation
- Polynomial/Quadratic: Fits a polynomial curve (e.g., quadratic...
