Handling missing values

Real-life data is far from perfect, and cases of having missing values are really common. The mechanisms in which the data has become unavailable are really important to come up with a good imputation strategy. We call imputation the process in which we deal with values that are missing in our data, which in most contexts are represented as NaN values. One of the most important aspects of this is to know which values are missing:

1. In the following code example, we will show how we can find out which columns have missing or null values by summing up all the Boolean output of the Spark isNull method by casting this Boolean output to integers:

   from pyspark.sql.functions import col, sum df.select(*(sum(col(c).isNull().cast("int")).alias(c) for c in df.columns)).show()

2. Another alternative would be to use the output of the Spark data frame describe method to filter out the count of missing values in each column and, finally, subtracting the count...