Title: Ignoring Null or Missing Values
Slug: ignoring_null_values
Summary: Ignoring Null or Missing Values in SQL. Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon

Note: This tutorial was written using Catherine Devlin's SQL in Jupyter Notebooks library. If you have not using a Jupyter Notebook, you can ignore the two lines of code below and any line containing %%sql. Furthermore, this tutorial uses SQLite's flavor of SQL, your version might have some differences in syntax.

For more, check out Learning SQL by Alan Beaulieu.



In [1]:

    
# Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False

Create Data



In [2]:

    
%%sql

-- Create a table of criminals
CREATE TABLE criminals (pid, name, age, sex, city, minor);
INSERT INTO criminals VALUES (412, 'James Smith', 15, 'M', 'Santa Rosa', 1);
INSERT INTO criminals VALUES (234, NULL, 22, 'M', 'Santa Rosa', 0);
INSERT INTO criminals VALUES (632, NULL, 23, 'F', 'San Francisco', 0);
INSERT INTO criminals VALUES (901, 'Gordon Ado', 32, 'F', 'San Francisco', 0);
INSERT INTO criminals VALUES (512, 'Bill Byson', 21, 'M', 'Petaluma', 0);









    Out[2]:





[]

Select Name And Ages Only When The Name Is Known



In [3]:

    
%%sql

--  Select name and average age,
SELECT name, age

--  from the table 'criminals',
FROM criminals

-- if age is not a null value
WHERE name IS NOT NULL









    Out[3]:





    
        name
        age
    
    
        James Smith
        15
    
    
        Gordon Ado
        32
    
    
        Bill Byson
        21