Missing values are a common challenge in data analysis and visualization. They can affect the quality, accuracy, and interpretation of your results. How can you identify and handle missing values in a dataset? In this article, you will learn some data cleaning best practices to deal with missing values in a systematic and effective way.
Missing values can occur for various reasons, such as data entry errors, incomplete surveys, technical issues, or intentional omission. Depending on the nature and source of the missing values, they can be classified into three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each type has different implications for the validity and reliability of your analysis and visualization.
Based on my reading on research papers, it is Not Missing At Random (NMAR). (not MNAR). NMAR can occur when sensors faced with out of the range readings. Since the sensor cannot observe the values, the value observation will be missing in the database.
The first step to handle missing values is to identify them in your dataset. You can use various tools and methods to detect missing values, such as summary statistics, frequency tables, histograms, heatmaps, or scatterplots. Some common indicators of missing values are blanks, zeros, nulls, NA, NaN, or other placeholders. You can also use code to check the percentage and pattern of missing values in your dataset. For example, in Python, you can use the isnull() and sum() methods to count the number of missing values in each column.
The second step to handle missing values is to decide how to deal with them in your dataset. You have two main options: removing or replacing them. Removing missing values means deleting the rows or columns that contain them. This can reduce the size and complexity of your dataset, but it can also introduce bias and reduce statistical power. Replacing missing values means imputing them with some values based on assumptions or calculations. This can preserve the structure and completeness of your dataset, but it can also introduce noise and uncertainty.
You should consider removing missing values when they are MCAR and represent a small proportion of your dataset. This means that the missing values are random and do not depend on any other variable in your dataset. In this case, removing them will not affect the distribution and relationship of your variables. However, if the missing values are MAR or MNAR, or represent a large proportion of your dataset, removing them may introduce bias and distortion in your analysis and visualization.
You should consider replacing missing values when they are MAR or MNAR and represent a large proportion of your dataset. This means that the missing values are not random and depend on some other variable in your dataset. In this case, replacing them with some reasonable values will help you maintain the integrity and consistency of your dataset. However, if the missing values are MCAR or represent a small proportion of your dataset, replacing them may introduce unnecessary variation and complexity in your analysis and visualization.
You should replace missing values with values that reflect the characteristics and context of your dataset. You can use different methods and techniques to impute missing values, such as mean, median, mode, constant, interpolation, regression, or machine learning. The choice of the method depends on the type, level, and distribution of your variable, as well as the objective and scope of your analysis and visualization. You should also evaluate the impact and accuracy of your imputation method on your results and conclusions.