Published on 21 Aug, 2019
Missing data is very common in our datasets. Their origin may be diverse - measurement drop, data write errors or records removing during the data cleansing process. For some statistical analyzes we need tables without missing data. We need to either remove these incomplete rows, fill in the empty cells, replace them by constant value or select rows with missing data.