Missing Value Analysis
One of the major steps in data analysis is missing value analysis. The primary reason we need to perform missing value analysis is to know how much data is missing in a column and how we are going to handle it.
In general, missing values can be handled in two ways. The first way is to drop the rows with missing values, unless the percentage of missing values is high (for example, 40% missing values in a column) as this will lead to loss of information.
The second method is imputing missing values, which is where we fill in the missing values based on the imputation method employed. For example, in mean imputation, we use the mean value of the particular column to fill in the missing values.
The next step is missing value analysis.
In order to find out how many missing values are present in the DataFrame, we are going to introduce you to a package called missingno, which will help you visualize the count of missing values in the DataFrames.
