![]() We can easily create series with help of a list, tuple, or dictionary. Pandas series is a one-dimensional array which is capable to store elements of various data types like list. Using Interpolation to fill Missing Values in Series Data We can also use Interpolation for calculating the moving averages. ![]() for example, suppose temperature, now we would always prefer to fill today’s temperature with the mean of the last 2 days, not with the mean of the month. ![]() Interpolation is mostly used while working with time-series data because in time-series data we like to fill missing values with previous one or two values. When imputing missing values with average does not fit best, we have to move to a different technique and the technique most people find is Interpolation. We can use Interpolation to find missing value with help of its neighbors.
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