Lab 13: SQL

Due by 11:59pm on Friday, August 7.

Starter Files

Download Inside the archive, you will find starter files for the questions in this lab, along with a copy of the Ok autograder.


By the end of this lab, you should have submitted the lab with python3 ok --submit. You may submit more than once before the deadline; only the final submission will be graded. Check that you have successfully submitted your code on


Consult this section if you need a refresher on the material for this lab, or if you're having trouble running SQL or SQLite on your computer. It's okay to skip directly to the questions and refer back here should you get stuck.

SQL Basics

Creating Tables

You can create SQL tables either from scratch or from existing tables.

The following statement creates a table by specifying column names and values without referencing another table. Each SELECT clause specifies the values for one row, and UNION is used to join rows together. The AS clauses give a name to each column; it need not be repeated in subsequent rows after the first.

CREATE TABLE [table_name] AS
  SELECT [val1] AS [column1], [val2] AS [column2], ... UNION
  SELECT [val3]             , [val4]             , ... UNION
  SELECT [val5]             , [val6]             , ...;

Let's say we want to make the following table called big_game which records the scores for the Big Game each year. This table has three columns: berkeley, stanford, and year.

We could do so with the following CREATE TABLE statement:

  SELECT 30 AS berkeley, 7 AS stanford, 2002 AS year UNION
  SELECT 28,             16,            2003         UNION
  SELECT 17,             38,            2014;

Selecting From Tables

More commonly, we will create new tables by selecting specific columns that we want from existing tables by using a SELECT statement as follows:

SELECT [columns] FROM [tables] WHERE [condition] ORDER BY [columns] LIMIT [limit];

Let's break down this statement:

  • SELECT [columns] tells SQL that we want to include the given columns in our output table; [columns] is a comma-separated list of column names, and * can be used to select all columns
  • FROM [table] tells SQL that the columns we want to select are from the given table; see the joins section to see how to select from multiple tables
  • WHERE [condition] filters the output table by only including rows whose values satisfy the given [condition], a boolean expression
  • ORDER BY [columns] orders the rows in the output table by the given comma-separated list of columns
  • LIMIT [limit] limits the number of rows in the output table by the integer [limit]

Note: We capitalize SQL keywords purely because of style convention. It makes queries much easier to read, though they will still work if you don't capitalize keywords.

Here are some examples:

Select all of Berkeley's scores from the big_game table, but only include scores from years past 2002:

sqlite> SELECT berkeley FROM big_game WHERE year > 2002;

Select the scores for both schools in years that Berkeley won:

sqlite> SELECT berkeley, stanford FROM big_game WHERE berkeley > stanford;

Select the years that Stanford scored more than 15 points:

sqlite> SELECT year FROM big_game WHERE stanford > 15;

SQL operators

Expressions in the SELECT, WHERE, and ORDER BY clauses can contain one or more of the following operators:

  • comparison operators: =, >, <, <=, >=, <> or != ("not equal")
  • boolean operators: AND, OR
  • arithmetic operators: +, -, *, /
  • concatenation operator: ||

Here are some examples:

Output the ratio of Berkeley's score to Stanford's score each year:

sqlite> select berkeley * 1.0 / stanford from big_game;

Output the sum of scores in years where both teams scored over 10 points:

sqlite> select berkeley + stanford from big_game where berkeley > 10 and stanford > 10;

Output a table with a single column and single row containing the value "hello world":

sqlite> SELECT "hello" || " " || "world";
hello world


To select data from multiple tables, we can use joins. There are many types of joins, but the only one we'll worry about is the inner join. To perform an inner join on two on more tables, simply list them all out in the FROM clause of a SELECT statement:

SELECT [columns] FROM [table1], [table2], ... WHERE [condition] ORDER BY [columns] LIMIT [limit];

We can select from multiple different tables or from the same table multiple times.

Let's say we have the following table that contains the names head football coaches at Cal since 2002:

  SELECT "Jeff Tedford" AS name, 2002 as start, 2012 as end UNION
  SELECT "Sonny Dykes"         , 2013         , 2016        UNION
  SELECT "Justin Wilcox"       , 2017         , null;

When we join two or more tables, the default output is a cartesian product. For example, if we joined big_game with coaches, we'd get the following:

If we want to match up each game with the coach that season, we'd have to compare columns from the two tables in the WHERE clause:

sqlite> SELECT * FROM big_game, coaches WHERE year >= start AND year <= end;
17|38|2014|Sonny Dykes|2013|2016
28|16|2003|Jeff Tedford|2002|2012
30|7|2002|Jeff Tedford|2002|2012

The following query outputs the coach and year for each Big Game win recorded in big_game:

sqlite> SELECT name, year FROM big_game, coaches
...>        WHERE berkeley > stanford AND year >= start AND year <= end;
Jeff Tedford|2003
Jeff Tedford|2002

In the queries above, none of the column names are ambiguous. For example, it is clear that the name column comes from the coaches table because there isn't a column in the big_game table with that name. However, if a column name exists in more than one of the tables being joined, or if we join a table with itself, we must disambiguate the column names using aliases.

For examples, let's find out what the score difference is for each team between a game in big_game and any previous games. Since each row in this table represents one game, in order to compare two games we must join big_game with itself:

sqlite> SELECT b.Berkeley - a.Berkeley, b.Stanford - a.Stanford, a.Year, b.Year
...>        FROM big_game AS a, big_game AS b WHERE a.Year < b.Year;

In the query above, we give the alias a to the first big_game table and the alias b to the second big_game table. We can then reference columns from each table using dot notation with the aliases, e.g. a.Berkeley, a.Stanford, and a.Year to select from the first table.


Python already comes with a built-in SQLite database engine to process SQL. However, it doesn't come with a "shell" to let you interact with it from the terminal. Because of this, until now, you have been using a simplified SQLite shell written by us. However, you may find the shell is old, buggy, or lacking in features. In that case, you may want to download and use the official SQLite executable.

If running python3 didn't work, you can download a precompiled sqlite directly by following the following instructions and then use sqlite3 and ./sqlite3 instead of python3 based on which is specified for your platform.

Another way to start using SQLite is to download a precompiled binary from the SQLite website. The latest version of SQLite at the time of writing is 3.28.0, but you can check for additional updates on the website.

However, before proceeding, please remove (or rename) any SQLite executables (sqlite3,, and the like) from the current folder, or they may conflict with the official one you download below. Similarly, if you wish to switch back later, please remove or rename the one you downloaded and restore the files you removed.


  1. Visit the download page linked above and navigate to the section Precompiled Binaries for Windows. Click on the link sqlite-tools-win32-x86-*.zip to download the binary.
  2. Unzip the file. There should be a sqlite3.exe file in the directory after extraction.
  3. Navigate to the folder containing the sqlite3.exe file and check that the version is at least 3.8.3:

    $ cd path/to/sqlite
    $ ./sqlite3 --version
    3.12.1 2016-04-08 15:09:49 fe7d3b75fe1bde41511b323925af8ae1b910bc4d

macOS Yosemite (10.10) or newer

SQLite comes pre-installed. Check that you have a version that's greater than 3.8.3:

    $ sqlite3
    SQLite version

Mac OS X Mavericks (10.9) or older

SQLite comes pre-installed, but it is the wrong version.

  1. Visit the download page linked above and navigate to the section Precompiled Binaries for Mac OS X (x86). Click on the link sqlite-tools-osx-x86-*.zip to download the binary.
  2. Unzip the file. There should be a sqlite3 file in the directory after extraction.
  3. Navigate to the folder containing the sqlite3 file and check that the version is at least 3.8.3:

    $ cd path/to/sqlite
    $ ./sqlite3 --version
    3.12.1 2016-04-08 15:09:49 fe7d3b75fe1bde41511b323925af8ae1b910bc4d


The easiest way to use SQLite on Ubuntu is to install it straight from the native repositories (the version will be slightly behind the most recent release):

$ sudo apt install sqlite3
$ sqlite3 --version
3.8.6 2014-08-15 11:46:33 9491ba7d738528f168657adb43a198238abde19e


First, check that a file named exists alongside the assignment files. If you don't see it, or if you encounter problems with it, scroll down to the Troubleshooting section to see how to download an official precompiled SQLite binary before proceeding.

You can start an interactive SQLite session in your Terminal or Git Bash with the following command:


While the interpreter is running, you can type .help to see some of the commands you can run.

To exit out of the SQLite interpreter, type .exit or .quit or press Ctrl-C. Remember that if you see ...> after pressing enter, you probably forgot a ;.

You can also run all the statements in a .sql file by doing the following:

  1. Runs your code and then exits SQLite immediately afterwards.

    python3 < lab13.sql
  2. Runs your code and then opens an interactive SQLite session, which is similar to running Python code with the interactive -i flag.

    python3 --init lab13.sql

The Survey Data!

On Tuesday, we asked you and your fellow students to complete a brief online survey through Google Forms, which involved relatively random but fun questions. In this lab, we will interact with the results of the survey by using SQL queries to see if we can find interesting things in the data.

First, take a look at data.sql and examine the table defined in it. Note its structure. You will be working with:

  • students: The main results of the survey. Each column represents a different question from the survey, except for the first column, which is the time of when the result was submitted. This time is a unique identifier for each of the rows in the table.

    Column Name Question
    time The unique timestamp that identifies the submission
    number What's your favorite number between 1 and 100?
    color What is your favorite color?
    seven Choose the number 7 below.
    • 7
    • Choose this option instead
    • seven
    • the number 7 below.
    • I find this question condescending
    song If you could listen to only one of these songs for the rest of your life, which would it be?
    • "Never Be Like You" by Flume
    • "Truth Hurts" by Lizzo
    • "Clair de Lune" by Claude Debussy
    • "Rock and Roll all Nite" by Kiss
    • "Dancing Queen" by ABBA
    • "So What" by Miles Davis
    • "Down With The Sickness" by Disturbed
    • "Seasons of Love" from Rent
    • "Formation" by Beyonce
    date Pick a day of the year!
    pet If you could have any animal in the world as a pet, what would it be?
    instructor Choose your favorite photo of John DeNero (Options shown under Question 3)
    smallest Try to guess the smallest unique positive INTEGER that anyone will put!
  • numbers: The results from the survey in which students could select more than one option from the numbers listed, which ranged from 0 to 10 and included 2018, 9000, and 9001. Each row has a time (which is again a unique identifier) and has the value 'True' if the student selected the column or 'False' if the student did not. The column names in this table are the following strings, referring to each possible number: '0', '1', '2', '4', '5', '6', '7', '8', '9', '10', '2018', '9000', '9001'.

Since the survey was anonymous, we used the timestamp that a survey was submitted as a unique identifier. A time in students matches up with a time in numbers. For example, a row in students whose time value is "2019/08/06 4:19:18 PM CDT" matches up with the row in numbers whose time value is "2019/08/06 4:19:18 PM CDT". These entries come from the same Google form submission and thus belong to the same student.

Note: If you are looking for your personal response within the data, you may have noticed that some of your answers are slightly different from what you had inputted. In order to make SQLite accept our data, and to optimize for as many matches as possible during our joins, we did the following things to clean up the data:

  • color and pet: We converted all the strings to be completely lowercase.
  • For some of the more "free-spirited" responses, we escaped the special characters so that they could be properly parsed.

You will write all of your solutions in the starter file lab13.sql provided. As with other labs, you can test your solutions with OK. In addition, you can use either of the following commands:

python3 < lab13.sql
python3 --init lab13.sql


Q1: What Would SQL print?

Note: there is no submission for this question

First, load the tables into sqlite3.

$ python3 --init lab13.sql

Before we start, inspect the schema of the tables that we've created for you:

sqlite> .schema

This tells you the name of each of our tables and their attributes.

Let's also take a look at some of the entries in our table. There are a lot of entries though, so let's just output the first 20:

sqlite> SELECT * FROM students LIMIT 20;

If you're curious about some of the answers students put into the Google form, open up data.sql in your favorite text editor and take a look!

For each of the SQL queries below, think about what the query is looking for, then try running the query yourself and see!

sqlite> SELECT * FROM students LIMIT 30; -- This is a comment. * is shorthand for all columns!
selects first 30 records from students;
sqlite> SELECT color FROM students WHERE number = 7;
selects the color from students who said their favorite number was 7;
sqlite> SELECT song, pet FROM students WHERE color = "blue" AND date = "12-25";
selects the song and pet from students who said their favorite color was blue and picked December 25th;

Remember to end each statement with a ;! To exit out of SQLite, type .exit or .quit or hit Ctrl-C.

Q2: Go Bears! (And Dogs?)

Now that we have learned how to select columns from a SQL table, let's filter the results to see some more interesting results!

It turns out that 61A students have a lot of school spirit: the most popular favorite color was 'blue'. You would think that this school spirit would carry over to the pet answer, and everyone would want a pet bear! Unfortunately, this was not the case, and the majority of students opted to have a pet 'dog' instead. That is the more sensible choice, I suppose...

Write a SQL query to create a table that contains both the column color and the column pet, using the keyword WHERE to restrict the answers to the most popular results of color being 'blue' and pet being 'dog'.

You should get the following output:

sqlite> SELECT * FROM bluedog;

This isn't a very exciting table, though. Each of these rows represents a different student, but all this table can really tell us is how many students both like the color blue and want a dog as a pet, because we didn't select for any other identifying characteristics. Let's create another table, bluedog_songs, that looks just like bluedog but also tells us how each student answered the song question.

You should get the following output:

sqlite> SELECT * FROM bluedog_songs;
blue|dog|Clair De Lune
blue|dog|Dancing Queen
blue|dog|Dancing Queen
blue|dog|Dancing Queen
blue|dog|Dancing Queen
blue|dog|Clair De Lune
blue|dog|Never Be Like You
blue|dog|Never Be Like You
CREATE TABLE bluedog_songs AS

This distrubiton of songs actually largely represents the distribution of song choices that the total group of students made, so perhaps all we've learned here is that there isn't a correlation between a student's favorite color and desired pet, and what song they could spend the rest of their life listening to. Even demonstrating that there is no correlation still reveals facts about our data though!

Use Ok to test your code:

python3 ok -q bluedog

Q3: Matchmaker, Matchmaker

Did you take 61A with the hope of finding your quarantine romance? Well you're in luck! With all this data in hand, it's easy for us to find your perfect match. If two students want the same pet and have the same taste in music, they are clearly meant to be together! In order to provide some more information for the potential lovebirds to converse about, let's include the favorite colors of the two individuals as well!

In order to match up students, you will have to do a join on the students table with itself. When you do a join, SQLite will match every single row with every single other row, so make sure you do not match anyone with themselves, or match any given pair twice!

Important Note: When pairing the first and second person, make sure that the first person responded first (i.e. they have an earlier time). This is to ensure your output matches our tests.

Hint: When joining table names where column names are the same, use dot notation to distinguish which columns are from which table: [table_name].[column name]. This sometimes may get verbose, so it’s stylistically better to give tables an alias using the AS keyword. The syntax for this is as follows:

SELECT <[alias1].[column name1], [alias2].[columnname2]...>
    FROM <[table_name1] AS [alias1],[table_name2] AS [alias2]...> ...

The query in the football example from earlier uses this syntax.

Write a SQL query to create a table that has 4 columns:

  • The shared preferred pet of the couple
  • The shared favorite song of the couple
  • The favorite color of the first person
  • The favorite color of the second person
CREATE TABLE matchmaker AS

Use Ok to test your code:

python3 ok -q matchmaker

Q4: Sevens

Let's take a look at data from both of our tables, students and numbers, to find out if students that really like the number 7 also chose '7' for the obedience question. Specifically, we want to look at the students that fulfill the below conditions and see if they also chose '7' in the question that asked students to choose the number 7 (column seven in students).


  • reported that their favorite number (column number in students) was 7
  • have 'True' in column '7' in numbers, meaning they checked the number 7 during the survey

In order to examine rows from both the students and the numbers table, we will need to perform a join.

How would you specify the WHERE clause to make the SELECT statement only consider rows in the joined table whose values all correspond to the same student? If you find that your output is massive and overwhelming, then you are probably missing the necessary condition in your WHERE clause to ensure this.

Note: The columns in the numbers table are strings with the associated number, so you must put quotes around the column name to refer to it. For example if you alias the table as a, to get the column to see if a student checked 9001, you must write a.'9001'.

Write a SQL query to create a table with just the column seven from students, filtering first for students who said their favorite number (column number) was 7 in the students table and who checked the box for seven (column '7') in the numbers table.


Use Ok to test your code:

python3 ok -q sevens

Q5: Let's Count

How many people liked each pet? What is the biggest date chosen this semester? How many obedient people chose Image 1 for the instructor? Is there a difference between last semester's average favorite number and this semester's?

To answer these types of questions, we can bring in SQL aggregation, which allows us to accumulate values across rows in our SQL database!

In order to perform SQL aggregation, we can group rows in our table by one or more attributes. Once we have groups, we can aggregate over the groups in our table and find things like:

  • the maximum value (MAX),
  • the minimum value (MIN),
  • the number of rows in the group (COUNT),
  • the average over all of the values (AVG),

and more! SELECT statements that use aggregation are usually marked by two things: an aggregate function (MAX, MIN, COUNT, AVG, etc.) and a GROUP BY clause. GROUP BY [column(s)] groups together rows with the same value in each column(s). In this section we will only use COUNT, which will count the number of rows in each group, but feel free to check out this link for more!

For example, the following query will print out the top 10 favorite numbers with their respective counts (yes, you are all hilarious):

sqlite> SELECT number, COUNT(*) AS count FROM students GROUP BY number ORDER BY count DESC LIMIT 10;

This SELECT statement first groups all of the rows in our table students by number. Then, within each group, we perform aggregation by COUNTing all the rows. By selecting number and COUNT(*), we then can see the highest number and how many students picked that number. We have to order by our COUNT(*), which is saved in the alias count, by DESCending order, so our highest count starts at the top, and we limit our result to the top 10.

You can then add ORDER BY column to the end of any query to sort the results by that column, in ascending order (or, with the parameter DESC you can sort in descending order instead, as above).

Let's have some fun with COUNT and ORDER BY! For each query below, we created its own table in lab13.sql, so fill in the corresponding table and run it using Ok.

Hint: You may find that there isn't a particular attribute you should have to perform the COUNT aggregation over. If you are only interested in counting the number of rows in a group, you can just say COUNT(*).

What are the top 10 pets this semester?

sqlite> SELECT * FROM favpets;

How many people marked exactly the word 'dog' as their ideal pet this semester?

sqlite> SELECT * FROM dog;

Now let's see how we can determine new information from a table we already made! In Question 1 we created a table bluedog_songs that selected the favorite songs of every student who said their favorite color was blue and their ideal pet was a dog. If we wanted to know what the favorite song was amongst these students, we would have to count by hand, but the whole point of SQL is that we shouldn't have to ever do such a thing. Instead, let's create a new table, bluedog_agg that sorts the songs in bluedog_songs by popularity and tells us how many students chose each song as their favorite. (Hint: Do not reinvent the wheel! Create bluedog_agg from the table that you've already made!)

sqlite> SELECT * FROM bluedog_agg;
Dancing Queen|4
Never Be Like You|2
Clair De Lune|2
CREATE TABLE bluedog_agg AS

Now, we can find the students' favorite for any column (try it yourself in the interpreter), but now let's try answering a more specific query. Let's see how many obedient students (i.e., students who answered the "seven" question correctly) picked each image of an instructor. We can do this by selecting only the rows that have seven = '7' then GROUP BY instructor, and finally we can COUNT them.

sqlite> SELECT * FROM instructor_obedience;
7|Image 1|6
7|Image 2|10
7|Image 3|12
7|Image 4|12
7|Image 5|10
CREATE TABLE instructor_obedience AS

The possibilities with COUNTing are endless, so have fun experimenting!

Use Ok to test your code:

python3 ok -q lets-count