Databricks Data Engineer Associate Question 114
Select 4You are working with a DataFrame in Databricks containing customer data. The DataFrame includes a column 'email' that may have null values. You need to calculate the total number of rows where the 'email' column is null and compare it to the total number of rows where the 'email' column is not null. Which approach should you use?
- A
Use count_if(email IS NULL) to count rows where the 'email' column is null.
- B
Use count_if(email IS NOT NULL) to count rows where the 'email' column is not null.
- C
Use count(*) - count(email) to count rows where the 'email' column is null.
- D
Use count(email) to directly count rows where the 'email' column is null.
- E
Use count(email) to count rows where the 'email' column is not null.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
To identify rows where a column is null or not null, you can use count_if with appropriate conditions (e.g., IS NULL or IS NOT NULL). Alternatively, for null counts, you can use the difference between count(*) and count(column), as count(column) only considers non-null values. This makes both approaches valid for the scenario.
- A. Correct.
This is a correct option because count_if(email IS NULL) effectively counts the rows where the 'email' column has null values.
- B. Correct.
This is a correct option because count_if(email IS NOT NULL) effectively counts the rows where the 'email' column has non-null values.
- C. Correct.
This is a correct option because count(*) gives the total row count, and subtracting count(email), which counts non-null values, results in the count of null values in the 'email' column.
- D. Incorrect.
This is incorrect because count(email) does not count rows with null values; it only counts non-null values.
- E. Correct.
This is a correct option because count(email) directly counts rows where the 'email' column is not null.