Missing Values
The data entries with no value assigned to them are called missing values. In the real world, encountering missing values in data is common. Values may be missing for a wide variety of reasons, such as non-responsiveness of the system/responder, data corruption, and partial deletion.
Some fields are more likely than other fields to contain missing values. For example, income data collected from surveys is likely to contain missing values, because of people not wanting to disclose their income.
Nevertheless, it is one of the major problems plaguing the data analytics world. Depending on the percentage of missing data, missing values may prove to be a significant challenge in data preparation and exploratory analysis. So, it's important to calculate the missing data percentage before getting started with data analysis.
In the following exercise, we will learn how to detect and calculate the number of missing value entries in PySpark DataFrames.