Removing missing values
There are a number of different techniques you can use to fix missing data in T-SQL and in this exercise, you will focus on returning rows with non-missing values. For example, to return all rows with non-missing SHAPE values, you can use:
SELECT *
FROM Incidents
WHERE Shape IS NOT NULL
This is a part of the course
“Intermediate SQL Server”
Exercise instructions
Write a T-SQL query which returns only the IncidentDateTime and IncidentState columns where IncidentState is not missing.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
-- Return the specified columns
___
FROM Incidents
-- Exclude all the missing values from IncidentState
WHERE ___This exercise is part of the course
Intermediate SQL Server
In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.
One of the first steps in data analysis is examining data through aggregations. This chapter explores how to create aggregations in SQL Server, a common first step in data exploration. You will also clean missing data and categorize data into bins with CASE statements.
Exercise 1: Data analysis with aggregationsExercise 2: Creating aggregationsExercise 3: Creating grouped aggregationsExercise 4: Dealing with missing dataExercise 5: Removing missing valuesExercise 6: Imputing missing values (I)Exercise 7: Imputing missing values (II)Exercise 8: Binning data with CASEExercise 9: Using CASE statementsExercise 10: Creating several groups with CASEWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.