All you need to know about Missing Values in your dataset
Identify and fill in missing values in Python
Missing data (or missing values) is defined as the data value that is not stored for a variable in the observation of interest. The problem of missing data is relatively common in almost all research and can have a significant effect on the conclusions that can be drawn from the data.
Types of Missing Values:
Missing completely at random (MCAR): When data are MCAR, the fact that the data are missing is independent of the observed and unobserved data. In other words, no systematic differences exist between participants with missing data and those with complete data. For example, some participants may have missing laboratory values because a batch of lab samples was processed improperly.
Missing at random (MAR): When data are MAR, the fact that the data are missing is systematically related to the observed but not the unobserved data. For example, a registry examining depression may encounter data that are MAR if male participants are less likely to complete a survey about depression severity than female participants.
Missing not at random (MNAR): When data are MNAR, the fact that the data are missing is systematically related to the unobserved data, that…
