Renumber chapters and listing numbers per new book order

Chapter_02/Chapter_02.sql

@@ -2,14 +2,14 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 1 Code Examples
+-- Chapter 2 Code Examples
 --------------------------------------------------------------
 
--- Listing 1-1: Creating a database named analysis
+-- Listing 2-1: Creating a database named analysis
 
 CREATE DATABASE analysis;
 
--- Listing 1-2: Creating a table named teachers with six columns
+-- Listing 2-2: Creating a table named teachers with six columns
 
 CREATE TABLE teachers (
     id bigserial,
@@ -23,7 +23,7 @@ CREATE TABLE teachers (
 -- This command will remove (drop) the table.
 -- DROP TABLE teachers;
 
--- Listing 1-3 Inserting data into the teachers table
+-- Listing 2-3 Inserting data into the teachers table
 
 INSERT INTO teachers (first_name, last_name, school, hire_date, salary)
 VALUES ('Janet', 'Smith', 'F.D. Roosevelt HS', '2011-10-30', 36200),

Chapter_03/Chapter_03.sql

@@ -2,41 +2,41 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 2 Code Examples
+-- Chapter 3 Code Examples
 --------------------------------------------------------------
 
--- Listing 2-1: Querying all rows and columns from the teachers table
+-- Listing 3-1: Querying all rows and columns from the teachers table
 
 SELECT * FROM teachers;
 
--- Listing 2-2: Querying a subset of columns
+-- Listing 3-2: Querying a subset of columns
 
 SELECT last_name, first_name, salary FROM teachers;
 
--- Listing 2-3: Querying distinct values in the school column
+-- Listing 3-3: Querying distinct values in the school column
 
 SELECT DISTINCT school
 FROM teachers;
 
--- Listing 2-4: Querying distinct pairs of values in the school and salary
+-- Listing 3-4: Querying distinct pairs of values in the school and salary
 -- columns
 
 SELECT DISTINCT school, salary
 FROM teachers;
 
--- Listing 2-5: Sorting a column with ORDER BY
+-- Listing 3-5: Sorting a column with ORDER BY
 
 SELECT first_name, last_name, salary
 FROM teachers
 ORDER BY salary DESC;
 
--- Listing 2-6: Sorting multiple columns with ORDER BY
+-- Listing 3-6: Sorting multiple columns with ORDER BY
 
 SELECT last_name, school, hire_date
 FROM teachers
 ORDER BY school ASC, hire_date DESC;
 
--- Listing 2-7: Filtering rows using WHERE
+-- Listing 3-7: Filtering rows using WHERE
 
 SELECT last_name, school, hire_date
 FROM teachers
@@ -69,7 +69,7 @@ SELECT first_name, last_name, school, salary
 FROM teachers
 WHERE salary BETWEEN 40000 AND 65000;
 
--- Listing 2-8: Filtering with LIKE AND ILIKE
+-- Listing 3-8: Filtering with LIKE AND ILIKE
 
 SELECT first_name
 FROM teachers
@@ -79,7 +79,7 @@ SELECT first_name
 FROM teachers
 WHERE first_name ILIKE 'sam%';
 
--- Listing 2-9: Combining operators using AND and OR
+-- Listing 3-9: Combining operators using AND and OR
 
 SELECT *
 FROM teachers
@@ -96,7 +96,7 @@ FROM teachers
 WHERE school = 'F.D. Roosevelt HS'
       AND (salary < 38000 OR salary > 40000);
 
--- Listing 2-10: A SELECT statement including WHERE and ORDER BY
+-- Listing 3-10: A SELECT statement including WHERE and ORDER BY
 
 SELECT first_name, last_name, school, hire_date, salary
 FROM teachers

Chapter_04/Chapter_04.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 3 Code Examples
+-- Chapter 4 Code Examples
 --------------------------------------------------------------
 
--- Listing 3-1: Character data types in action
+-- Listing 4-1: Character data types in action
 
 CREATE TABLE char_data_types (
     varchar_column varchar(10),
@@ -22,7 +22,7 @@ COPY char_data_types TO 'C:\YourDirectory\typetest.txt'
 WITH (FORMAT CSV, HEADER, DELIMITER '|');
 
 
--- Listing 3-2: Number data types in action
+-- Listing 4-2: Number data types in action
 
 CREATE TABLE number_data_types (
     numeric_column numeric(20,5),
@@ -38,8 +38,8 @@ VALUES
 
 SELECT * FROM number_data_types;
 
--- Listing 3-3: Rounding issues with float columns
+-- Listing 4-3: Rounding issues with float columns
--- Assumes table created and loaded with Listing 3-2
+-- Assumes table created and loaded with Listing 4-2
 
 SELECT
     numeric_column * 10000000 AS "Fixed",
@@ -47,7 +47,7 @@ SELECT
 FROM number_data_types
 WHERE numeric_column = .7;
 
--- Listing 3-4: Timestamp and interval types in action
+-- Listing 4-4: Timestamp and interval types in action
 
 CREATE TABLE date_time_types (
     timestamp_column timestamp with time zone,
@@ -63,8 +63,8 @@ VALUES
 
 SELECT * FROM date_time_types;
 
--- Listing 3-5: Using the interval data type
+-- Listing 4-5: Using the interval data type
--- Assumes script 3-4 has been run
+-- Assumes script 4-4 has been run
 
 SELECT
     timestamp_column,
@@ -72,7 +72,7 @@ SELECT
     timestamp_column - interval_column AS new_date
 FROM date_time_types;
 
--- Listing 3-6: Three CAST() examples
+-- Listing 4-6: Three CAST() examples
 
 SELECT timestamp_column, CAST(timestamp_column AS varchar(10))
 FROM date_time_types;

Chapter_05/Chapter_05.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 4 Code Examples
+-- Chapter 5 Code Examples
 --------------------------------------------------------------
 
--- Listing 4-1: Using COPY for data import
+-- Listing 5-1: Using COPY for data import
 -- This is example syntax only; running it will produce an error
 
 COPY table_name
@@ -13,7 +13,7 @@ FROM 'C:\YourDirectory\your_file.csv'
 WITH (FORMAT CSV, HEADER);
 
 
--- Listing 4-2: A CREATE TABLE statement for Census county data
+-- Listing 5-2: A CREATE TABLE statement for Census county data
 -- Full data dictionary available at: http://www.census.gov/prod/cen2010/doc/pl94-171.pdf
 -- Note: Some columns have been given more descriptive names
 
@@ -124,7 +124,7 @@ CREATE TABLE us_counties_2010 (
 
 SELECT * FROM us_counties_2010;
 
--- Listing 4-3: Importing Census data using COPY
+-- Listing 5-3: Importing Census data using COPY
 -- Note! If you run into an import error here, be sure you downloaded the code and
 -- data for the book according to the steps listed on page xxvii in the Introduction.
 -- Windows users: Please check the Note on page xxvii as well.
@@ -148,7 +148,7 @@ ORDER BY internal_point_lon DESC
 LIMIT 5;
 
 
--- Listing 4-4: Creating a table to track supervisor salaries
+-- Listing 5-4: Creating a table to track supervisor salaries
 
 CREATE TABLE supervisor_salaries (
     town varchar(30),
@@ -159,7 +159,7 @@ CREATE TABLE supervisor_salaries (
     benefits money
 );
 
--- Listing 4-5: Importing salaries data from CSV to three table columns
+-- Listing 5-5: Importing salaries data from CSV to three table columns
 
 COPY supervisor_salaries (town, supervisor, salary)
 FROM 'C:\YourDirectory\supervisor_salaries.csv'
@@ -168,7 +168,7 @@ WITH (FORMAT CSV, HEADER);
 -- Check the data
 SELECT * FROM supervisor_salaries LIMIT 2;
 
--- Listing 4-6 Use a temporary table to add a default value to a column during
+-- Listing 5-6 Use a temporary table to add a default value to a column during
 -- import
 
 DELETE FROM supervisor_salaries;
@@ -188,20 +188,20 @@ DROP TABLE supervisor_salaries_temp;
 -- Check the data
 SELECT * FROM supervisor_salaries LIMIT 2;
 
--- Listing 4-7: Export an entire table with COPY
+-- Listing 5-7: Export an entire table with COPY
 
 COPY us_counties_2010
 TO 'C:\YourDirectory\us_counties_export.txt'
 WITH (FORMAT CSV, HEADER, DELIMITER '|');
 
 
--- Listing 4-8: Exporting selected columns from a table with COPY
+-- Listing 5-8: Exporting selected columns from a table with COPY
 
 COPY us_counties_2010 (geo_name, internal_point_lat, internal_point_lon)
 TO 'C:\YourDirectory\us_counties_latlon_export.txt'
 WITH (FORMAT CSV, HEADER, DELIMITER '|');
 
--- Listing 4-9: Exporting query results with COPY
+-- Listing 5-9: Exporting query results with COPY
 
 COPY (
     SELECT geo_name, state_us_abbreviation

Chapter_06/Chapter_06.sql

@@ -2,23 +2,23 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 5 Code Examples
+-- Chapter 6 Code Examples
 --------------------------------------------------------------
 
--- Listing 5-1: Basic addition, subtraction and multiplication with SQL
+-- Listing 6-1: Basic addition, subtraction and multiplication with SQL
 
 SELECT 2 + 2;    -- addition
 SELECT 9 - 1;    -- subtraction
 SELECT 3 * 4;    -- multiplication
 
--- Listing 5-2: Integer and decimal division with SQL
+-- Listing 6-2: Integer and decimal division with SQL
 
 SELECT 11 / 6;   -- integer division
 SELECT 11 % 6;   -- modulo division
 SELECT 11.0 / 6; -- decimal division
 SELECT CAST(11 AS numeric(3,1)) / 6;
 
--- Listing 5-3: Exponents, roots and factorials with SQL
+-- Listing 6-3: Exponents, roots and factorials with SQL
 
 SELECT 3 ^ 4;    -- exponentiation
 SELECT |/ 10;    -- square root (operator)
@@ -34,7 +34,7 @@ SELECT (7 + 8) * 9;	-- answer: 135
 SELECT 3 ^ 3 - 1;   -- answer: 26
 SELECT 3 ^ (3 - 1); -- answer: 9
 
--- Listing 5-4: Selecting Census population columns by race with aliases
+-- Listing 6-4: Selecting Census population columns by race with aliases
 
 SELECT geo_name,
        state_us_abbreviation AS "st",
@@ -48,7 +48,7 @@ SELECT geo_name,
        p0010009 AS "Two or More Races"
 FROM us_counties_2010;
 
--- Listing 5-5: Adding two columns in us_counties_2010
+-- Listing 6-5: Adding two columns in us_counties_2010
 
 SELECT geo_name,
        state_us_abbreviation AS "st",
@@ -57,7 +57,7 @@ SELECT geo_name,
        p0010003 + p0010004 AS "Total White and Black"
 FROM us_counties_2010;
 
--- Listing 5-6: Checking Census data totals
+-- Listing 6-6: Checking Census data totals
 
 SELECT geo_name,
        state_us_abbreviation AS "st",
@@ -69,7 +69,7 @@ SELECT geo_name,
 FROM us_counties_2010
 ORDER BY "Difference" DESC;
 
--- Listing 5-7: Calculating the percent of the population that is 
+-- Listing 6-7: Calculating the percent of the population that is 
 -- Asian by county (percent of the whole)
 
 SELECT geo_name,
@@ -78,7 +78,7 @@ SELECT geo_name,
 FROM us_counties_2010
 ORDER BY "pct_asian" DESC;
 
--- Listing 5-8: Calculating percent change
+-- Listing 6-8: Calculating percent change
 
 CREATE TABLE percent_change (
     department varchar(20),
@@ -102,13 +102,13 @@ SELECT department,
                     spend_2014 * 100, 1 ) AS "pct_change"
 FROM percent_change;
 
--- Listing 5-9: Using sum() and avg() aggregate functions
+-- Listing 6-9: Using sum() and avg() aggregate functions
 
 SELECT sum(p0010001) AS "County Sum",
        round(avg(p0010001), 0) AS "County Average"
 FROM us_counties_2010;
 
--- Listing 5-10: Testing SQL percentile functions
+-- Listing 6-10: Testing SQL percentile functions
 
 CREATE TABLE percentile_test (
     numbers integer
@@ -124,7 +124,7 @@ SELECT
     WITHIN GROUP (ORDER BY numbers)
 FROM percentile_test;
 
--- Listing 5-11: Using sum(), avg(), and percentile_cont() aggregate functions
+-- Listing 6-11: Using sum(), avg(), and percentile_cont() aggregate functions
 
 SELECT sum(p0010001) AS "County Sum",
        round(avg(p0010001), 0) AS "County Average",
@@ -132,7 +132,7 @@ SELECT sum(p0010001) AS "County Sum",
        WITHIN GROUP (ORDER BY p0010001) AS "County Median"
 FROM us_counties_2010;
 
--- Listing 5-12: Passing an array of values to percentile_cont()
+-- Listing 6-12: Passing an array of values to percentile_cont()
 
 -- quartiles
 SELECT percentile_cont(array[.25,.5,.75])
@@ -150,7 +150,7 @@ SELECT percentile_cont(array[.1,.2,.3,.4,.5,.6,.7,.8,.9])
        WITHIN GROUP (ORDER BY p0010001) AS "deciles"
 FROM us_counties_2010;
 
--- Listing 5-13: Using unnest() to turn an array into rows
+-- Listing 6-13: Using unnest() to turn an array into rows
 
 SELECT unnest(
             percentile_cont(array[.25,.5,.75])
@@ -158,7 +158,7 @@ SELECT unnest(
             ) AS "quartiles"
 FROM us_counties_2010;
 
--- Listing 5-14: Creating a median() aggregate function in PostgreSQL
+-- Listing 6-14: Creating a median() aggregate function in PostgreSQL
 -- Source: https://wiki.postgresql.org/wiki/Aggregate_Median
 
 CREATE OR REPLACE FUNCTION _final_median(anyarray)
@@ -192,7 +192,7 @@ CREATE AGGREGATE median(anyelement) (
   INITCOND='{}'
 );
 
--- Listing 5-15: Using a median() aggregate function
+-- Listing 6-15: Using a median() aggregate function
 
 SELECT sum(p0010001) AS "County Sum",
        round(avg(p0010001), 0) AS "County Average",
@@ -201,7 +201,7 @@ SELECT sum(p0010001) AS "County Sum",
        WITHIN GROUP (ORDER BY P0010001) AS "50th Percentile"
 FROM us_counties_2010;
 
--- Listing 5-16: Finding the most-frequent value with mode()
+-- Listing 6-16: Finding the most-frequent value with mode()
 
 SELECT mode() WITHIN GROUP (ORDER BY p0010001)
 FROM us_counties_2010;

Chapter_07/Chapter_07.sql

@@ -5,7 +5,7 @@
 -- Chapter 6 Code Examples
 --------------------------------------------------------------
 
--- Listing 6-1: Creating the departments and employees tables
+-- Listing 7-1: Creating the departments and employees tables
 
 CREATE TABLE departments (
     dept_id bigserial,
@@ -37,13 +37,13 @@ VALUES
     ('Soo', 'Nguyen', 83000, 2),
     ('Janet', 'King', 95000, 2);
 
--- Listing 6-2: Joining the employees and departments tables
+-- Listing 7-2: Joining the employees and departments tables
 
 SELECT *
 FROM employees JOIN departments
 ON employees.dept_id = departments.dept_id;
 
--- Listing 6-3: Creating two tables to explore JOIN types
+-- Listing 7-3: Creating two tables to explore JOIN types
 
 CREATE TABLE schools_left (
     id integer CONSTRAINT left_id_key PRIMARY KEY,
@@ -68,7 +68,7 @@ INSERT INTO schools_right (id, right_school) VALUES
     (4, 'Chase Magnet Academy'),
     (6, 'Jefferson High School');
 
--- Listing 6-4: Using JOIN
+-- Listing 7-4: Using JOIN
 
 SELECT *
 FROM schools_left JOIN schools_right
@@ -80,51 +80,51 @@ SELECT *
 FROM schools_left INNER JOIN schools_right
 ON schools_left.id = schools_right.id;
 
--- Listing 6-5: Using LEFT JOIN
+-- Listing 7-5: Using LEFT JOIN
 
 SELECT *
 FROM schools_left LEFT JOIN schools_right
 ON schools_left.id = schools_right.id;
 
--- Listing 6-6: Using RIGHT JOIN
+-- Listing 7-6: Using RIGHT JOIN
 
 SELECT *
 FROM schools_left RIGHT JOIN schools_right
 ON schools_left.id = schools_right.id;
 
--- Listing 6-7: Using FULL OUTER JOIN
+-- Listing 7-7: Using FULL OUTER JOIN
 
 SELECT *
 FROM schools_left FULL OUTER JOIN schools_right
 ON schools_left.id = schools_right.id;
 
--- Listing 6-8: Using CROSS JOIN
+-- Listing 7-8: Using CROSS JOIN
 
 SELECT *
 FROM schools_left CROSS JOIN schools_right;
 
--- Listing 6-9: Filtering to show missing values with IS NULL
+-- Listing 7-9: Filtering to show missing values with IS NULL
 
 SELECT *
 FROM schools_left LEFT JOIN schools_right
 ON schools_left.id = schools_right.id
 WHERE schools_right.id IS NULL;
 
--- Listing 6-10: Querying specific columns in a join
+-- Listing 7-10: Querying specific columns in a join
 SELECT schools_left.id,
        schools_left.left_school,
        schools_right.right_school
 FROM schools_left LEFT JOIN schools_right
 ON schools_left.id = schools_right.id;
 
--- Listing 6-11: Simplifying code with table aliases
+-- Listing 7-11: Simplifying code with table aliases
 SELECT lt.id,
        lt.left_school,
        rt.right_school
 FROM schools_left AS lt LEFT JOIN schools_right AS rt
 ON lt.id = rt.id;
 
--- Listing 6-12: Joining multiple tables
+-- Listing 7-12: Joining multiple tables
 CREATE TABLE schools_enrollment (
     id integer,
     enrollment integer
@@ -155,7 +155,7 @@ FROM schools_left AS lt LEFT JOIN schools_enrollment AS en
 LEFT JOIN schools_grades AS gr
     ON lt.id = gr.id;
 
--- Listing 6-13: Performing math on joined Census tables
+-- Listing 7-13: Performing math on joined Census tables
 -- Decennial Census 2000. Full data dictionary at https://www.census.gov/prod/cen2000/doc/pl94-171.pdf
 -- Note: Some non-number columns have been given more descriptive names
 

Chapter_08/Chapter_08.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 7 Code Examples
+-- Chapter 8 Code Examples
 --------------------------------------------------------------
 
--- Listing 7-1: Declaring a single-column natural key as primary key
+-- Listing 8-1: Declaring a single-column natural key as primary key
 
 -- As a column constraint
 CREATE TABLE natural_key_example (
@@ -25,14 +25,14 @@ CREATE TABLE natural_key_example (
     CONSTRAINT license_key PRIMARY KEY (license_id)
 );
 
--- Listing 7-2: Example of a primary key violation
+-- Listing 8-2: Example of a primary key violation
 INSERT INTO natural_key_example (license_id, first_name, last_name)
 VALUES ('T229901', 'Lynn', 'Malero');
 
 INSERT INTO natural_key_example (license_id, first_name, last_name)
 VALUES ('T229901', 'Sam', 'Tracy');
 
--- Listing 7-3: Declaring a composite primary key as a natural key
+-- Listing 8-3: Declaring a composite primary key as a natural key
 CREATE TABLE natural_key_composite_example (
     student_id varchar(10),
     school_day date,
@@ -40,7 +40,7 @@ CREATE TABLE natural_key_composite_example (
     CONSTRAINT student_key PRIMARY KEY (student_id, school_day)
 );
 
--- Listing 7-4: Example of a composite primary key violation
+-- Listing 8-4: Example of a composite primary key violation
 
 INSERT INTO natural_key_composite_example (student_id, school_day, present)
 VALUES(775, '1/22/2017', 'Y');
@@ -51,7 +51,7 @@ VALUES(775, '1/23/2017', 'Y');
 INSERT INTO natural_key_composite_example (student_id, school_day, present)
 VALUES(775, '1/23/2017', 'N');
 
--- Listing 7-5: Declaring a bigserial column as a surrogate key
+-- Listing 8-5: Declaring a bigserial column as a surrogate key
 
 CREATE TABLE surrogate_key_example (
     order_number bigserial,
@@ -67,7 +67,7 @@ VALUES ('Beachball Polish', '2015-03-17'),
 
 SELECT * FROM surrogate_key_example;
 
--- Listing 7-6: A foreign key example
+-- Listing 8-6: A foreign key example
 
 CREATE TABLE licenses (
     license_id varchar(10),
@@ -92,7 +92,7 @@ VALUES ('A203391', '3/17/2017', 'T229901');
 INSERT INTO registrations (registration_id, registration_date, license_id)
 VALUES ('A75772', '3/17/2017', 'T000001');
 
--- Listing 7-7: CHECK constraint examples
+-- Listing 8-7: CHECK constraint examples
 
 CREATE TABLE check_constraint_example (
     user_id bigserial,
@@ -110,7 +110,7 @@ VALUES ('admin');
 INSERT INTO check_constraint_example (salary)
 VALUES (0);
 
--- Listing 7-8: UNIQUE constraint example
+-- Listing 8-8: UNIQUE constraint example
 
 CREATE TABLE unique_constraint_example (
     contact_id bigserial CONSTRAINT contact_id_key PRIMARY KEY,
@@ -129,7 +129,7 @@ VALUES ('Betty', 'Diaz', 'bdiaz@example.org');
 INSERT INTO unique_constraint_example (first_name, last_name, email)
 VALUES ('Sasha', 'Lee', 'slee@example.org');
 
--- Listing 7-9: NOT NULL constraint example
+-- Listing 8-9: NOT NULL constraint example
 
 CREATE TABLE not_null_example (
     student_id bigserial,
@@ -138,7 +138,7 @@ CREATE TABLE not_null_example (
     CONSTRAINT student_id_key PRIMARY KEY (student_id)
 );
 
--- Listing 7-10: Dropping and adding a primary key and a NOT NULL constraint
+-- Listing 8-10: Dropping and adding a primary key and a NOT NULL constraint
 
 -- Drop
 ALTER TABLE not_null_example DROP CONSTRAINT student_id_key;
@@ -152,7 +152,7 @@ ALTER TABLE not_null_example ALTER COLUMN first_name DROP NOT NULL;
 -- Add
 ALTER TABLE not_null_example ALTER COLUMN first_name SET NOT NULL;
 
--- Listing 7-11: Importing New York City address data
+-- Listing 8-11: Importing New York City address data
 
 CREATE TABLE new_york_addresses (
     longitude numeric(9,6),
@@ -168,7 +168,7 @@ COPY new_york_addresses
 FROM 'C:\YourDirectory\city_of_new_york.csv'
 WITH (FORMAT CSV, HEADER);
 
--- Listing 7-12: Benchmark queries for index performance
+-- Listing 8-12: Benchmark queries for index performance
 
 EXPLAIN ANALYZE SELECT * FROM new_york_addresses
 WHERE street = 'BROADWAY';
@@ -179,6 +179,6 @@ WHERE street = '52 STREET';
 EXPLAIN ANALYZE SELECT * FROM new_york_addresses
 WHERE street = 'ZWICKY AVENUE';
 
--- Listing 7-13: Creating a B-Tree index on the new_york_addresses table
+-- Listing 8-13: Creating a B-Tree index on the new_york_addresses table
 
 CREATE INDEX street_idx ON new_york_addresses (street);

Chapter_09/Chapter_09.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 8 Code Examples
+-- Chapter 9 Code Examples
 --------------------------------------------------------------
 
--- Listing 8-1: Creating and filling the 2014 Public Libraries Survey table
+-- Listing 9-1: Creating and filling the 2014 Public Libraries Survey table
 
 CREATE TABLE pls_fy2014_pupld14a (
     stabr varchar(2) NOT NULL,
@@ -91,7 +91,7 @@ COPY pls_fy2014_pupld14a
 FROM 'C:\YourDirectory\pls_fy2014_pupld14a.csv'
 WITH (FORMAT CSV, HEADER);
 
--- Listing 8-2: Creating and filling the 2009 Public Libraries Survey table
+-- Listing 9-2: Creating and filling the 2009 Public Libraries Survey table
 
 CREATE TABLE pls_fy2009_pupld09a (
     stabr varchar(2) NOT NULL,
@@ -173,7 +173,7 @@ COPY pls_fy2009_pupld09a
 FROM 'C:\YourDirectory\pls_fy2009_pupld09a.csv'
 WITH (FORMAT CSV, HEADER);
 
--- Listing 8-3: Using count() for table row counts
+-- Listing 9-3: Using count() for table row counts
 
 SELECT count(*)
 FROM pls_fy2014_pupld14a;
@@ -181,12 +181,12 @@ FROM pls_fy2014_pupld14a;
 SELECT count(*)
 FROM pls_fy2009_pupld09a;
 
--- Listing 8-4: Using count() for the number of values in a column
+-- Listing 9-4: Using count() for the number of values in a column
 
 SELECT count(salaries)
 FROM pls_fy2014_pupld14a;
 
--- Listing 8-5: Using count() for the number of distinct values in a column
+-- Listing 9-5: Using count() for the number of distinct values in a column
 
 SELECT count(libname)
 FROM pls_fy2014_pupld14a;
@@ -205,11 +205,11 @@ SELECT libname, city, stabr
 FROM pls_fy2014_pupld14a
 WHERE libname = 'OXFORD PUBLIC LIBRARY';
 
--- Listing 8-6: Finding the most and fewest visits using max() and min()
+-- Listing 9-6: Finding the most and fewest visits using max() and min()
 SELECT max(visits), min(visits)
 FROM pls_fy2014_pupld14a;
 
--- Listing 8-7: Using GROUP BY on the stabr column
+-- Listing 9-7: Using GROUP BY on the stabr column
 
 -- There are 56 in 2014.
 SELECT stabr
@@ -223,7 +223,7 @@ FROM pls_fy2009_pupld09a
 GROUP BY stabr
 ORDER BY stabr;
 
--- Listing 8-8: Using GROUP BY on the city and stabr columns
+-- Listing 9-8: Using GROUP BY on the city and stabr columns
 
 SELECT city, stabr
 FROM pls_fy2014_pupld14a
@@ -236,21 +236,21 @@ FROM pls_fy2014_pupld14a
 GROUP BY city, stabr
 ORDER BY count(*) DESC;
 
--- Listing 8-9: GROUP BY with count() on the stabr column
+-- Listing 9-9: GROUP BY with count() on the stabr column
 
 SELECT stabr, count(*)
 FROM pls_fy2014_pupld14a
 GROUP BY stabr
 ORDER BY count(*) DESC;
 
--- Listing 8-10: GROUP BY with count() on the stabr and stataddr columns
+-- Listing 9-10: GROUP BY with count() on the stabr and stataddr columns
 
 SELECT stabr, stataddr, count(*)
 FROM pls_fy2014_pupld14a
 GROUP BY stabr, stataddr
 ORDER BY stabr ASC, count(*) DESC;
 
--- Listing 8-11: Using the sum() aggregate function to total visits to
+-- Listing 9-11: Using the sum() aggregate function to total visits to
 -- libraries in 2014 and 2009
 
 -- 2014
@@ -263,7 +263,7 @@ SELECT sum(visits) AS visits_2009
 FROM pls_fy2009_pupld09a
 WHERE visits >= 0;
 
--- Listing 8-12: Using sum() to total visits on joined 2014 and 2009 library tables
+-- Listing 9-12: Using sum() to total visits on joined 2014 and 2009 library tables
 
 SELECT sum(pls14.visits) AS visits_2014,
        sum(pls09.visits) AS visits_2009
@@ -271,7 +271,7 @@ FROM pls_fy2014_pupld14a pls14 JOIN pls_fy2009_pupld09a pls09
 ON pls14.fscskey = pls09.fscskey
 WHERE pls14.visits >= 0 AND pls09.visits >= 0;
 
--- Listing 8-13: Using GROUP BY to track percent change in library visits by state
+-- Listing 9-13: Using GROUP BY to track percent change in library visits by state
 
 SELECT pls14.stabr,
        sum(pls14.visits) AS visits_2014,
@@ -284,7 +284,7 @@ WHERE pls14.visits >= 0 AND pls09.visits >= 0
 GROUP BY pls14.stabr
 ORDER BY pct_change DESC;
 
--- Listing 8-14: Using HAVING to filter the results of an aggregate query
+-- Listing 9-14: Using HAVING to filter the results of an aggregate query
 
 SELECT pls14.stabr,
        sum(pls14.visits) AS visits_2014,

Chapter_10/Chapter_10.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 9 Code Examples
+-- Chapter 10 Code Examples
 --------------------------------------------------------------
 
--- Listing 9-1: Importing the FSIS Meat, Poultry, and Egg Inspection Directory
+-- Listing 10-1: Importing the FSIS Meat, Poultry, and Egg Inspection Directory
 -- https://catalog.data.gov/dataset/meat-poultry-and-egg-inspection-directory-by-establishment-name
 
 CREATE TABLE meat_poultry_egg_inspect (
@@ -30,7 +30,7 @@ CREATE INDEX company_idx ON meat_poultry_egg_inspect (company);
 -- Count the rows imported:
 SELECT count(*) FROM meat_poultry_egg_inspect;
 
--- Listing 9-2: Finding multiple companies at the same address
+-- Listing 10-2: Finding multiple companies at the same address
 SELECT company,
        street,
        city,
@@ -41,14 +41,14 @@ GROUP BY company, street, city, st
 HAVING count(*) > 1
 ORDER BY company, street, city, st;
 
--- Listing 9-3: Grouping and counting states
+-- Listing 10-3: Grouping and counting states
 SELECT st, 
        count(*) AS st_count
 FROM meat_poultry_egg_inspect
 GROUP BY st
 ORDER BY st;
 
--- Listing 9-4: Using IS NULL to find missing values in the st column
+-- Listing 10-4: Using IS NULL to find missing values in the st column
 SELECT est_number,
        company,
        city,
@@ -57,7 +57,7 @@ SELECT est_number,
 FROM meat_poultry_egg_inspect
 WHERE st IS NULL;
 
--- Listing 9-5: Using GROUP BY and count() to find inconsistent company names
+-- Listing 10-5: Using GROUP BY and count() to find inconsistent company names
 
 SELECT company,
        count(*) AS company_count
@@ -65,7 +65,7 @@ FROM meat_poultry_egg_inspect
 GROUP BY company
 ORDER BY company ASC;
 
--- Listing 9-6: Using length() and count() to test the zip column
+-- Listing 10-6: Using length() and count() to test the zip column
 
 SELECT length(zip),
        count(*) AS length_count
@@ -73,7 +73,7 @@ FROM meat_poultry_egg_inspect
 GROUP BY length(zip)
 ORDER BY length(zip) ASC;
 
--- Listing 9-7: Filtering with length() to find short zip values
+-- Listing 10-7: Filtering with length() to find short zip values
 
 SELECT st,
        count(*) AS st_count
@@ -82,7 +82,7 @@ WHERE length(zip) < 5
 GROUP BY st
 ORDER BY st ASC;
 
--- Listing 9-8: Backing up a table
+-- Listing 10-8: Backing up a table
 
 CREATE TABLE meat_poultry_egg_inspect_backup AS
 SELECT * FROM meat_poultry_egg_inspect;
@@ -92,21 +92,21 @@ SELECT
     (SELECT count(*) FROM meat_poultry_egg_inspect) AS original,
     (SELECT count(*) FROM meat_poultry_egg_inspect_backup) AS backup;
 
--- Listing 9-9: Creating and filling the st_copy column with ALTER TABLE and UPDATE
+-- Listing 10-9: Creating and filling the st_copy column with ALTER TABLE and UPDATE
 
 ALTER TABLE meat_poultry_egg_inspect ADD COLUMN st_copy varchar(2);
 
 UPDATE meat_poultry_egg_inspect
 SET st_copy = st;
 
--- Listing 9-10: Checking values in the st and st_copy columns
+-- Listing 10-10: Checking values in the st and st_copy columns
 
 SELECT st,
        st_copy
 FROM meat_poultry_egg_inspect
 ORDER BY st;
 
--- Listing 9-11: Updating the st column for three establishments
+-- Listing 10-11: Updating the st column for three establishments
 
 UPDATE meat_poultry_egg_inspect
 SET st = 'MN'
@@ -120,7 +120,7 @@ UPDATE meat_poultry_egg_inspect
 SET st = 'WI'
 WHERE est_number = 'M263A+P263A+V263A';
 
--- Listing 9-12: Restoring original st column values
+-- Listing 10-12: Restoring original st column values
 
 -- Restoring from the column backup
 UPDATE meat_poultry_egg_inspect
@@ -132,14 +132,14 @@ SET st = backup.st
 FROM meat_poultry_egg_inspect_backup backup
 WHERE original.est_number = backup.est_number; 
 
--- Listing 9-13: Creating and filling the company_standard column
+-- Listing 10-13: Creating and filling the company_standard column
 
 ALTER TABLE meat_poultry_egg_inspect ADD COLUMN company_standard varchar(100);
 
 UPDATE meat_poultry_egg_inspect
 SET company_standard = company;
 
--- Listing 9-14: Use UPDATE to modify field values that match a string
+-- Listing 10-14: Use UPDATE to modify field values that match a string
 
 UPDATE meat_poultry_egg_inspect
 SET company_standard = 'Armour-Eckrich Meats'
@@ -149,26 +149,26 @@ SELECT company, company_standard
 FROM meat_poultry_egg_inspect
 WHERE company LIKE 'Armour%';
 
--- Listing 9-15: Creating and filling the zip_copy column
+-- Listing 10-15: Creating and filling the zip_copy column
 
 ALTER TABLE meat_poultry_egg_inspect ADD COLUMN zip_copy varchar(5);
 
 UPDATE meat_poultry_egg_inspect
 SET zip_copy = zip;
 
--- Listing 9-16: Modify codes in the zip column missing two leading zeros
+-- Listing 10-16: Modify codes in the zip column missing two leading zeros
 
 UPDATE meat_poultry_egg_inspect
 SET zip = '00' || zip
 WHERE st IN('PR','VI') AND length(zip) = 3;
 
--- Listing 9-17: Modify codes in the zip column missing one leading zero
+-- Listing 10-17: Modify codes in the zip column missing one leading zero
 
 UPDATE meat_poultry_egg_inspect
 SET zip = '0' || zip
 WHERE st IN('CT','MA','ME','NH','NJ','RI','VT') AND length(zip) = 4;
 
--- Listing 9-18: Creating and filling a state_regions table
+-- Listing 10-18: Creating and filling a state_regions table
 
 CREATE TABLE state_regions (
     st varchar(2) CONSTRAINT st_key PRIMARY KEY,
@@ -179,38 +179,38 @@ COPY state_regions
 FROM 'C:\YourDirectory\state_regions.csv'
 WITH (FORMAT CSV, HEADER, DELIMITER ',');
 
--- Listing 9-19: Adding and updating an inspection_date column
+-- Listing 10-19: Adding and updating an inspection_date column
 
 ALTER TABLE meat_poultry_egg_inspect ADD COLUMN inspection_date date;
 
 UPDATE meat_poultry_egg_inspect inspect
-SET inspection_date = '2019-12-01'
+SET inspection_date = '20110-12-01'
 WHERE EXISTS (SELECT state_regions.region
               FROM state_regions
               WHERE inspect.st = state_regions.st 
                     AND state_regions.region = 'New England');
 
--- Listing 9-20: Viewing updated inspection_date values
+-- Listing 10-20: Viewing updated inspection_date values
 
 SELECT st, inspection_date
 FROM meat_poultry_egg_inspect
 GROUP BY st, inspection_date
 ORDER BY st;
 
--- Listing 9-21: Delete rows matching an expression
+-- Listing 10-21: Delete rows matching an expression
 
 DELETE FROM meat_poultry_egg_inspect
 WHERE st IN('PR','VI');
 
--- Listing 9-22: Remove a column from a table using DROP
+-- Listing 10-22: Remove a column from a table using DROP
 
 ALTER TABLE meat_poultry_egg_inspect DROP COLUMN zip_copy;
 
--- Listing 9-23: Remove a table from a database using DROP
+-- Listing 10-23: Remove a table from a database using DROP
 
 DROP TABLE meat_poultry_egg_inspect_backup;
 
--- Listing 9-24: Demonstrating a transaction block
+-- Listing 10-24: Demonstrating a transaction block
 
 -- Start transaction and perform update
 START TRANSACTION;
@@ -243,14 +243,14 @@ WHERE company = 'AGRO Merchants Oakland, LLC';
 
 COMMIT;
 
--- Listing 9-25: Backing up a table while adding and filling a new column
+-- Listing 10-25: Backing up a table while adding and filling a new column
 
 CREATE TABLE meat_poultry_egg_inspect_backup AS
 SELECT *,
        '2018-02-07'::date AS reviewed_date
 FROM meat_poultry_egg_inspect;
 
--- Listing 9-26: Swapping table names using ALTER TABLE
+-- Listing 10-26: Swapping table names using ALTER TABLE
 
 ALTER TABLE meat_poultry_egg_inspect RENAME TO meat_poultry_egg_inspect_temp;
 ALTER TABLE meat_poultry_egg_inspect_backup RENAME TO meat_poultry_egg_inspect;

Chapter_11/Chapter_11.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 10 Code Examples
+-- Chapter 11 Code Examples
 --------------------------------------------------------------
 
--- Listing 10-1: Create Census 2011-2015 ACS 5-Year stats table and import data
+-- Listing 11-1: Create Census 2011-2015 ACS 5-Year stats table and import data
 
 CREATE TABLE acs_2011_2015_stats (
     geoid varchar(14) CONSTRAINT geoid_key PRIMARY KEY,
@@ -24,14 +24,14 @@ WITH (FORMAT CSV, HEADER, DELIMITER ',');
 
 SELECT * FROM acs_2011_2015_stats;
 
--- Listing 10-2: Using corr(Y, X) to measure the relationship between 
+-- Listing 11-2: Using corr(Y, X) to measure the relationship between 
 -- education and income
 
 SELECT corr(median_hh_income, pct_bachelors_higher)
     AS bachelors_income_r
 FROM acs_2011_2015_stats;
 
--- Listing 10-3: Using corr(Y, X) on additional variables
+-- Listing 11-3: Using corr(Y, X) on additional variables
 
 SELECT
     round(
@@ -45,7 +45,7 @@ SELECT
       ) AS bachelors_travel_r
 FROM acs_2011_2015_stats;
 
--- Listing 10-4: Regression slope and intercept functions
+-- Listing 11-4: Regression slope and intercept functions
 
 SELECT
     round(
@@ -56,7 +56,7 @@ SELECT
         ) AS y_intercept
 FROM acs_2011_2015_stats;
 
--- Listing 10-5: Calculating the coefficient of determination, or r-squared
+-- Listing 11-5: Calculating the coefficient of determination, or r-squared
 
 SELECT round(
         regr_r2(median_hh_income, pct_bachelors_higher)::numeric, 3
@@ -76,7 +76,7 @@ FROM acs_2011_2015_stats;
 SELECT covar_pop(median_hh_income, pct_bachelors_higher)
 FROM acs_2011_2015_stats;
 
--- Listing 10-6: The rank() and dense_rank() window functions
+-- Listing 11-6: The rank() and dense_rank() window functions
 
 CREATE TABLE widget_companies (
     id bigserial,
@@ -102,7 +102,7 @@ SELECT
     dense_rank() OVER (ORDER BY widget_output DESC)
 FROM widget_companies;
 
--- Listing 10-7: Applying rank() within groups using PARTITION BY
+-- Listing 11-7: Applying rank() within groups using PARTITION BY
 
 CREATE TABLE store_sales (
     store varchar(30),
@@ -130,7 +130,7 @@ SELECT
     rank() OVER (PARTITION BY category ORDER BY unit_sales DESC)
 FROM store_sales;
 
--- Listing 10-8: Create and fill a 2015 FBI crime data table
+-- Listing 11-8: Create and fill a 2015 FBI crime data table
 
 CREATE TABLE fbi_crime_data_2015 (
     st varchar(20),
@@ -151,7 +151,7 @@ WITH (FORMAT CSV, HEADER, DELIMITER ',');
 SELECT * FROM fbi_crime_data_2015
 ORDER BY population DESC;
 
--- Listing 10-9: Find property crime rates per thousand in cities with 500,000
+-- Listing 11-9: Find property crime rates per thousand in cities with 500,000
 -- or more people
 
 SELECT

Chapter_12/Chapter_12.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 11 Code Examples
+-- Chapter 12 Code Examples
 --------------------------------------------------------------
 
--- Listing 11-1: Extracting components of a timestamp value using date_part()
+-- Listing 12-1: Extracting components of a timestamp value using date_part()
 
 SELECT
     date_part('year', '2019-12-01 18:37:12 EST'::timestamptz) AS "year",
@@ -23,7 +23,7 @@ SELECT
 
 SELECT extract('year' from '2019-12-01 18:37:12 EST'::timestamptz) AS "year";
 
--- Listing 11-2: Three functions for making datetimes from components
+-- Listing 12-2: Three functions for making datetimes from components
 
 -- make a date
 SELECT make_date(2018, 2, 22);
@@ -42,7 +42,7 @@ SELECT
     localtimestamp,
     now();
 
--- Listing 11-3: Comparing current_timestamp and clock_timestamp() during row insert
+-- Listing 12-3: Comparing current_timestamp and clock_timestamp() during row insert
 
 CREATE TABLE current_time_example (
     time_id bigserial,
@@ -59,11 +59,11 @@ SELECT * FROM current_time_example;
 
 -- Time Zones
 
--- Listing 11-4: Showing your PostgreSQL server's default time zone
+-- Listing 12-4: Showing your PostgreSQL server's default time zone
 
 SHOW timezone; -- Note: You can see all run-time defaults with SHOW ALL;
 
--- Listing 11-5: Showing time zone abbreviations and names
+-- Listing 12-5: Showing time zone abbreviations and names
 
 SELECT * FROM pg_timezone_abbrevs;
 SELECT * FROM pg_timezone_names;
@@ -72,7 +72,7 @@ SELECT * FROM pg_timezone_names;
 SELECT * FROM pg_timezone_names
 WHERE name LIKE 'Europe%';
 
--- Listing 11-6: Setting the time zone for a client session
+-- Listing 12-6: Setting the time zone for a client session
 
 SET timezone TO 'US/Pacific';
 
@@ -101,7 +101,7 @@ SELECT '9/30/1929'::date + '5 years'::interval;
 
 -- Taxi Rides
 
--- Listing 11-7: Creating a table and importing NYC yellow taxi data
+-- Listing 12-7: Creating a table and importing NYC yellow taxi data
 
 CREATE TABLE nyc_yellow_taxi_trips_2016_06_01 (
     trip_id bigserial PRIMARY KEY,
@@ -155,7 +155,7 @@ ON nyc_yellow_taxi_trips_2016_06_01 (tpep_pickup_datetime);
 
 SELECT count(*) FROM nyc_yellow_taxi_trips_2016_06_01;
 
--- Listing 11-8: Counting taxi trips by hour
+-- Listing 12-8: Counting taxi trips by hour
 
 SELECT
     date_part('hour', tpep_pickup_datetime) AS trip_hour,
@@ -164,7 +164,7 @@ FROM nyc_yellow_taxi_trips_2016_06_01
 GROUP BY trip_hour
 ORDER BY trip_hour;
 
--- Listing 11-9: Exporting taxi pickups per hour to a CSV file
+-- Listing 12-9: Exporting taxi pickups per hour to a CSV file
 
 COPY
     (SELECT
@@ -177,7 +177,7 @@ COPY
 TO 'C:\YourDirectory\hourly_pickups_2016_06_01.csv'
 WITH (FORMAT CSV, HEADER, DELIMITER ',');
 
--- Listing 11-10: Calculating median trip time by hour
+-- Listing 12-10: Calculating median trip time by hour
 
 SELECT
     date_part('hour', tpep_pickup_datetime) AS trip_hour,
@@ -188,7 +188,7 @@ FROM nyc_yellow_taxi_trips_2016_06_01
 GROUP BY trip_hour
 ORDER BY trip_hour;
 
--- Listing 11-11: Creating a table to hold train trip data
+-- Listing 12-11: Creating a table to hold train trip data
 
 SET timezone TO 'US/Central';
 
@@ -210,21 +210,21 @@ VALUES
 
 SELECT * FROM train_rides;
 
--- Listing 11-12: Calculating the length of each trip segment
+-- Listing 12-12: Calculating the length of each trip segment
 
 SELECT segment,
        to_char(departure, 'YYYY-MM-DD HH12:MI a.m. TZ') AS departure,
        arrival - departure AS segment_time
 FROM train_rides;
 
--- Listing 11-13: Calculating cumulative intervals using OVER
+-- Listing 12-13: Calculating cumulative intervals using OVER
 
 SELECT segment,
        arrival - departure AS segment_time,
        sum(arrival - departure) OVER (ORDER BY trip_id) AS cume_time
 FROM train_rides;
 
--- Listing 11-14: Better formatting for cumulative trip time
+-- Listing 12-14: Better formatting for cumulative trip time
 
 SELECT segment,
        arrival - departure AS segment_time,

Chapter_13/Chapter_13.sql

@@ -2,10 +2,10 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 12 Code Examples
+-- Chapter 13 Code Examples
 --------------------------------------------------------------
 
--- Listing 12-1: Using a subquery in a WHERE clause
+-- Listing 13-1: Using a subquery in a WHERE clause
 
 SELECT geo_name,
        state_us_abbreviation,
@@ -17,7 +17,7 @@ WHERE p0010001 >= (
     )
 ORDER BY p0010001 DESC;
 
--- Listing 12-2: Using a subquery in a WHERE clause for DELETE
+-- Listing 13-2: Using a subquery in a WHERE clause for DELETE
 
 CREATE TABLE us_counties_2010_top10 AS
 SELECT * FROM us_counties_2010;
@@ -30,7 +30,7 @@ WHERE p0010001 < (
 
 SELECT count(*) FROM us_counties_2010_top10;
 
--- Listing 12-3: Subquery as a derived table in a FROM clause
+-- Listing 13-3: Subquery as a derived table in a FROM clause
 
 SELECT round(calcs.average, 0) as average,
        calcs.median,
@@ -43,7 +43,7 @@ FROM (
      )
 AS calcs;
 
--- Listing 12-4: Joining two derived tables
+-- Listing 13-4: Joining two derived tables
 
 SELECT census.state_us_abbreviation AS st,
        census.st_population,
@@ -69,7 +69,7 @@ JOIN
 ON plants.st = census.state_us_abbreviation
 ORDER BY plants_per_million DESC;
 
--- Listing 12-5: Adding a subquery to a column list
+-- Listing 13-5: Adding a subquery to a column list
 
 SELECT geo_name,
        state_us_abbreviation AS st,
@@ -78,7 +78,7 @@ SELECT geo_name,
         FROM us_counties_2010) AS us_median
 FROM us_counties_2010;
 
--- Listing 12-6: Using a subquery expression in a calculation
+-- Listing 13-6: Using a subquery expression in a calculation
 
 SELECT geo_name,
        state_us_abbreviation AS st,
@@ -136,7 +136,7 @@ WHERE EXISTS (
 
                    
                    
--- Listing 12-7: Using a simple CTE to find large counties
+-- Listing 13-7: Using a simple CTE to find large counties
 
 WITH
     large_counties (geo_name, st, p0010001)
@@ -158,7 +158,7 @@ WHERE p0010001 >= 100000
 GROUP BY state_us_abbreviation
 ORDER BY count(*) DESC;
 
--- Listing 12-8: Using CTEs in a table join
+-- Listing 13-8: Using CTEs in a table join
 
 WITH
     counties (st, population) AS
@@ -179,7 +179,7 @@ FROM counties JOIN plants
 ON counties.st = plants.st
 ORDER BY per_million DESC;
 
--- Listing 12-9: Using CTEs to minimize redundant code
+-- Listing 13-9: Using CTEs to minimize redundant code
 
 WITH us_median AS 
     (SELECT percentile_cont(.5) 
@@ -201,7 +201,7 @@ WHERE (p0010001 - us_median_pop)
 
 CREATE EXTENSION tablefunc;
 
--- Listing 12-10: Creating and filling the ice_cream_survey table
+-- Listing 13-10: Creating and filling the ice_cream_survey table
 
 CREATE TABLE ice_cream_survey (
     response_id integer PRIMARY KEY,
@@ -213,7 +213,7 @@ COPY ice_cream_survey
 FROM 'C:\YourDirectory\ice_cream_survey.csv'
 WITH (FORMAT CSV, HEADER);
 
--- Listing 12-11: Generating the ice cream survey crosstab
+-- Listing 13-11: Generating the ice cream survey crosstab
 
 SELECT *
 FROM crosstab('SELECT office,
@@ -233,7 +233,7 @@ AS (office varchar(20),
     strawberry bigint,
     vanilla bigint);
 
--- Listing 12-12: Creating and filling a temperature_readings table
+-- Listing 13-12: Creating and filling a temperature_readings table
 
 CREATE TABLE temperature_readings (
     reading_id bigserial PRIMARY KEY,
@@ -248,7 +248,7 @@ COPY temperature_readings
 FROM 'C:\YourDirectory\temperature_readings.csv'
 WITH (FORMAT CSV, HEADER);
 
--- Listing 12-13: Generating the temperature readings crosstab
+-- Listing 13-13: Generating the temperature readings crosstab
 
 SELECT *
 FROM crosstab('SELECT
@@ -279,7 +279,7 @@ AS (station varchar(50),
     dec numeric(3,0)
 );
 
--- Listing 12-14: Re-classifying temperature data with CASE
+-- Listing 13-14: Re-classifying temperature data with CASE
 
 SELECT max_temp,
        CASE WHEN max_temp >= 90 THEN 'Hot'
@@ -291,7 +291,7 @@ SELECT max_temp,
         END AS temperature_group
 FROM temperature_readings;
 
--- Listing 12-15: Using CASE in a Common Table Expression
+-- Listing 13-15: Using CASE in a Common Table Expression
 
 WITH temps_collapsed (station_name, max_temperature_group) AS
     (SELECT station_name,

Chapter_14/Chapter_14.sql

@@ -2,7 +2,7 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 13 Code Examples
+-- Chapter 14 Code Examples
 --------------------------------------------------------------
 
 -- Commonly used string functions
@@ -34,7 +34,7 @@ SELECT right('703-555-1212', 8);
 SELECT replace('bat', 'b', 'c');
 
 
--- Table 13-2: Regular Expression Matching Examples
+-- Table 14-2: Regular Expression Matching Examples
 
 -- Any character one or more times
 SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from '.+');
@@ -54,7 +54,7 @@ SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from 'May \d, \d{4}
 
 -- Turning Text to Data with Regular Expression Functions
 
--- Listing 13-2: Creating and loading the crime_reports table
+-- Listing 14-2: Creating and loading the crime_reports table
 -- Data from https://sheriff.loudoun.gov/dailycrime
 
 CREATE TABLE crime_reports (
@@ -75,29 +75,29 @@ WITH (FORMAT CSV, HEADER OFF, QUOTE '"');
 
 SELECT original_text FROM crime_reports;
 
--- Listing 13-3: Using regexp_match() to find the first date
+-- Listing 14-3: Using regexp_match() to find the first date
 SELECT crime_id,
        regexp_match(original_text, '\d{1,2}\/\d{1,2}\/\d{2}')
 FROM crime_reports;
 
--- Listing 13-4: Using the regexp_matches() function with the 'g' flag
+-- Listing 14-4: Using the regexp_matches() function with the 'g' flag
 SELECT crime_id,
        regexp_matches(original_text, '\d{1,2}\/\d{1,2}\/\d{2}', 'g')
 FROM crime_reports;
 
--- Listing 13-5: Using regexp_match() to find the second date
+-- Listing 14-5: Using regexp_match() to find the second date
 -- Note that the result includes an unwanted hyphen
 SELECT crime_id,
        regexp_match(original_text, '-\d{1,2}\/\d{1,2}\/\d{1,2}')
 FROM crime_reports;
 
--- Listing 13-6: Using a capture group to return only the date
+-- Listing 14-6: Using a capture group to return only the date
 -- Eliminates the hyphen
 SELECT crime_id,
        regexp_match(original_text, '-(\d{1,2}\/\d{1,2}\/\d{1,2})')
 FROM crime_reports;
 
--- Listing 13-7: Matching case number, date, crime type, and city
+-- Listing 14-7: Matching case number, date, crime type, and city
 
 SELECT
     regexp_match(original_text, '(?:C0|SO)[0-9]+') AS case_number,
@@ -127,7 +127,7 @@ SELECT crime_id,
        regexp_match(original_text, '(?:C0|SO)[0-9]+') AS case_number
 FROM crime_reports;
 
--- Listing 13-8: Retrieving a value from within an array
+-- Listing 14-8: Retrieving a value from within an array
 
 SELECT
     crime_id,
@@ -135,7 +135,7 @@ SELECT
         AS case_number
 FROM crime_reports;
 
--- Listing 13-9: Updating the crime_reports date_1 column
+-- Listing 14-9: Updating the crime_reports date_1 column
 
 UPDATE crime_reports
 SET date_1 = 
@@ -151,7 +151,7 @@ SELECT crime_id,
        original_text
 FROM crime_reports;
 
--- Listing 13-10: Updating all crime_reports columns
+-- Listing 14-10: Updating all crime_reports columns
 
 UPDATE crime_reports
 SET date_1 = 
@@ -193,7 +193,7 @@ SET date_1 =
     description = (regexp_match(original_text, ':\s(.+)(?:C0|SO)'))[1],
     case_number = (regexp_match(original_text, '(?:C0|SO)[0-9]+'))[1];
 
--- Listing 13-11: Viewing selected crime data
+-- Listing 14-11: Viewing selected crime data
 
 SELECT date_1,
        street,
@@ -201,7 +201,7 @@ SELECT date_1,
        crime_type
 FROM crime_reports;
 
--- Listing 13-12: Using regular expressions in a WHERE clause
+-- Listing 14-12: Using regular expressions in a WHERE clause
 
 SELECT geo_name
 FROM us_counties_2010
@@ -214,7 +214,7 @@ WHERE geo_name ~* '.+ash.+' AND geo_name !~ 'Wash.+'
 ORDER BY geo_name;
 
 
--- Listing 13-13: Regular expression functions to replace and split
+-- Listing 14-13: Regular expression functions to replace and split
 
 SELECT regexp_replace('05/12/2018', '\d{4}', '2017');
 
@@ -222,7 +222,7 @@ SELECT regexp_split_to_table('Four,score,and,seven,years,ago', ',');
 
 SELECT regexp_split_to_array('Phil Mike Tony Steve', ' ');
 
--- Listing 13-14: Finding an array length
+-- Listing 14-14: Finding an array length
 
 SELECT array_length(regexp_split_to_array('Phil Mike Tony Steve', ' '), 1);
 
@@ -234,21 +234,21 @@ SELECT array_length(regexp_split_to_array('Phil Mike Tony Steve', ' '), 1);
 -- | (OR)
 -- ! (NOT)
 
--- Listing 13-15: Converting text to tsvector data
+-- Listing 14-15: Converting text to tsvector data
 
 SELECT to_tsvector('I am walking across the sitting room to sit with you.');
 
--- Listing 13-16: Converting search terms to tsquery data
+-- Listing 14-16: Converting search terms to tsquery data
 
 SELECT to_tsquery('walking & sitting');
 
--- Listing 13-17: Querying a tsvector type with a tsquery
+-- Listing 14-17: Querying a tsvector type with a tsquery
 
 SELECT to_tsvector('I am walking across the sitting room') @@ to_tsquery('walking & sitting');
 
 SELECT to_tsvector('I am walking across the sitting room') @@ to_tsquery('walking & running');
 
--- Listing 13-18: Creating and filling the president_speeches table
+-- Listing 14-18: Creating and filling the president_speeches table
 
 -- Sources:
 -- https://archive.org/details/State-of-the-Union-Addresses-1945-2006
@@ -270,23 +270,23 @@ WITH (FORMAT CSV, DELIMITER '|', HEADER OFF, QUOTE '@');
 
 SELECT * FROM president_speeches;
 
--- Listing 13-19: Converting speeches to tsvector in the search_speech_text column
+-- Listing 14-19: Converting speeches to tsvector in the search_speech_text column
 
 UPDATE president_speeches
 SET search_speech_text = to_tsvector('english', speech_text);
 
--- Listing 13-20: Creating a GIN index for text search
+-- Listing 14-20: Creating a GIN index for text search
 
 CREATE INDEX search_idx ON president_speeches USING gin(search_speech_text);
 
--- Listing 13-21: Finding speeches containing the word "Vietnam"
+-- Listing 14-21: Finding speeches containing the word "Vietnam"
 
 SELECT president, speech_date
 FROM president_speeches
 WHERE search_speech_text @@ to_tsquery('Vietnam')
 ORDER BY speech_date;
 
--- Listing 13-22: Displaying search results with ts_headline()
+-- Listing 14-22: Displaying search results with ts_headline()
 
 SELECT president,
        speech_date,
@@ -299,7 +299,7 @@ SELECT president,
 FROM president_speeches
 WHERE search_speech_text @@ to_tsquery('Vietnam');
 
--- Listing 13-23: Finding speeches with the word "transportation" but not "roads"
+-- Listing 14-23: Finding speeches with the word "transportation" but not "roads"
 
 SELECT president,
        speech_date,
@@ -312,7 +312,7 @@ SELECT president,
 FROM president_speeches
 WHERE search_speech_text @@ to_tsquery('transportation & !roads');
 
--- Listing 13-24: Find speeches where "defense" follows "military"
+-- Listing 14-24: Find speeches where "defense" follows "military"
 
 SELECT president,
        speech_date,
@@ -337,7 +337,7 @@ SELECT president,
 FROM president_speeches
 WHERE search_speech_text @@ to_tsquery('military <2> defense');
 
--- Listing 13-25: Scoring relevance with ts_rank()
+-- Listing 14-25: Scoring relevance with ts_rank()
 
 SELECT president,
        speech_date,
@@ -348,7 +348,7 @@ WHERE search_speech_text @@ to_tsquery('war & security & threat & enemy')
 ORDER BY score DESC
 LIMIT 5;
 
--- Listing 13-26: Normalizing ts_rank() by speech length
+-- Listing 14-26: Normalizing ts_rank() by speech length
 
 SELECT president,
        speech_date,

Chapter_15/Chapter_15.sql

@@ -2,27 +2,27 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 14 Code Examples
+-- Chapter 15 Code Examples
 --------------------------------------------------------------
 
--- Listing 14-1: Creating a gis_analysis database
+-- Listing 15-1: Creating a gis_analysis database
 
 CREATE DATABASE gis_analysis;
 -- Note: Switch to this new database before continuing the examples
 
--- Listing 14-2: Loading the PostGIS extension
+-- Listing 15-2: Loading the PostGIS extension
 
 CREATE EXTENSION postgis;
 
 SELECT postgis_full_version(); -- shows PostGIS version
 
--- Listing 14-3: Retrieving the well-known text for SRID 4326
+-- Listing 15-3: Retrieving the well-known text for SRID 4326
 
 SELECT srtext
 FROM spatial_ref_sys
 WHERE srid = 4326;
 
--- Listing 14-4: Using ST_GeomFromText() to create spatial objects
+-- Listing 15-4: Using ST_GeomFromText() to create spatial objects
 
 SELECT ST_GeomFromText('POINT(-74.9233606 42.699992)', 4326);
 
@@ -44,24 +44,24 @@ SELECT ST_GeomFromText('MULTIPOLYGON((
                                       -74.98 42.64, -74.98 42.66,
                                       -75.0 42.66)))', 4326);
 
--- Listing 14-5: Using ST_GeogFromText() to create spatial objects
+-- Listing 15-5: Using ST_GeogFromText() to create spatial objects
 
 SELECT
 ST_GeogFromText('SRID=4326;MULTIPOINT(-74.9 42.7, -75.1 42.7, -74.924 42.6)');
 
--- Listing 14-6: Functions specific to making points
+-- Listing 15-6: Functions specific to making points
 
 SELECT ST_PointFromText('POINT(-74.9233606 42.699992)', 4326);
 
 SELECT ST_MakePoint(-74.9233606, 42.699992);
 SELECT ST_SetSRID(ST_MakePoint(-74.9233606, 42.699992), 4326);
 
--- Listing 14-7: Functions specific to making LineStrings
+-- Listing 15-7: Functions specific to making LineStrings
 
 SELECT ST_LineFromText('LINESTRING(-105.90 35.67,-105.91 35.67)', 4326);
 SELECT ST_MakeLine(ST_MakePoint(-74.92, 42.69), ST_MakePoint(-74.12, 42.45));
 
--- Listing 14-8: Functions specific to making Polygons
+-- Listing 15-8: Functions specific to making Polygons
 
 SELECT ST_PolygonFromText('POLYGON((-74.9 42.7, -75.1 42.7,
                                     -75.1 42.6, -74.9 42.7))', 4326);
@@ -84,7 +84,7 @@ SELECT ST_MPolyFromText('MULTIPOLYGON((
 -- https://www.ams.usda.gov/local-food-directories/farmersmarkets
 
 
--- Listing 14-9: Create and load the farmers_markets table
+-- Listing 15-9: Create and load the farmers_markets table
 
 CREATE TABLE farmers_markets (
     fmid bigint PRIMARY KEY,
@@ -105,7 +105,7 @@ WITH (FORMAT CSV, HEADER);
 
 SELECT count(*) FROM farmers_markets; -- should return 8,681 rows
 
--- Listing 14-10: Creating and indexing a geography column
+-- Listing 15-10: Creating and indexing a geography column
 -- There's also a function: https://postgis.net/docs/AddGeometryColumn.html
 
 -- Add column
@@ -129,7 +129,7 @@ FROM farmers_markets
 WHERE longitude IS NOT NULL
 LIMIT 5;
 
--- Listing 14-11: Using ST_DWithin() to locate farmers' markets within 10 kilometers of a point
+-- Listing 15-11: Using ST_DWithin() to locate farmers' markets within 10 kilometers of a point
 
 SELECT market_name,
        city,
@@ -140,7 +140,7 @@ WHERE ST_DWithin(geog_point,
                  10000)
 ORDER BY market_name;
 
--- Listing 14-12: Using ST_Distance() to calculate the miles between Yankee Stadium
+-- Listing 15-12: Using ST_Distance() to calculate the miles between Yankee Stadium
 -- and Citi Field (Mets)
 -- 1609.344 meters/mile
 
@@ -149,7 +149,7 @@ SELECT ST_Distance(
                    ST_GeogFromText('POINT(-73.8480153 40.7570917)')
                    ) / 1609.344 AS mets_to_yanks;
 
--- Listing 14-13: Using ST_Distance() for each row in farmers_markets
+-- Listing 15-13: Using ST_Distance() for each row in farmers_markets
 
 SELECT market_name,
        city,
@@ -173,13 +173,13 @@ ORDER BY miles_from_dt ASC;
    -- Cartographic Boundary Shapefiles - Counties
    -- https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html
 
--- Listing 14-14: Checking the geom column's well-known text representation
+-- Listing 15-14: Checking the geom column's well-known text representation
 
 SELECT ST_AsText(geom)
 FROM us_counties_2010_shp
 LIMIT 1;
 
--- Listing 14-15: Find the largest counties by area using ST_Area()
+-- Listing 15-15: Find the largest counties by area using ST_Area()
 
 SELECT name10,
        statefp10 AS st,
@@ -190,7 +190,7 @@ FROM us_counties_2010_shp
 ORDER BY square_miles DESC
 LIMIT 5;
 
--- Listing 14-16: Using ST_Within() to find the county belonging to a pair of coordinates
+-- Listing 15-16: Using ST_Within() to find the county belonging to a pair of coordinates
 
 SELECT name10,
        statefp10
@@ -219,7 +219,7 @@ WHERE ST_Within('SRID=4269;POINT(-118.3419063 34.0977076)'::geometry, geom);
 -- https://www.census.gov/geo/reference/mtfcc.html
 -- Here, H3010: A natural flowing waterway
 
--- Listing 14-17: Using ST_GeometryType() to determine geometry
+-- Listing 15-17: Using ST_GeometryType() to determine geometry
 
 SELECT ST_GeometryType(geom)
 FROM santafe_linearwater_2016
@@ -229,7 +229,7 @@ SELECT ST_GeometryType(geom)
 FROM santafe_roads_2016
 LIMIT 1;
 
--- Listing 14-18: Spatial join with ST_Intersects() to find roads crossing the Santa Fe river
+-- Listing 15-18: Spatial join with ST_Intersects() to find roads crossing the Santa Fe river
 
 SELECT water.fullname AS waterway,
        roads.rttyp,
@@ -239,7 +239,7 @@ FROM santafe_linearwater_2016 water JOIN santafe_roads_2016 roads
 WHERE water.fullname = 'Santa Fe Riv'
 ORDER BY roads.fullname;
 
--- Listing 14-19: Using ST_Intersection() to show where roads cross the river
+-- Listing 15-19: Using ST_Intersection() to show where roads cross the river
 
 SELECT water.fullname AS waterway,
        roads.rttyp,

Chapter_17/Chapter_17.sql

@@ -2,12 +2,12 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 15 Code Examples
+-- Chapter 17 Code Examples
 --------------------------------------------------------------
 
 -- VIEWS
 
--- Listing 15-1: Creating a view that displays Nevada 2010 counties
+-- Listing 17-1: Creating a view that displays Nevada 2010 counties
 
 CREATE OR REPLACE VIEW nevada_counties_pop_2010 AS
     SELECT geo_name,
@@ -18,13 +18,13 @@ CREATE OR REPLACE VIEW nevada_counties_pop_2010 AS
     WHERE state_us_abbreviation = 'NV'
     ORDER BY county_fips;
 
--- Listing 15-2: Querying the nevada_counties_pop_2010 view
+-- Listing 17-2: Querying the nevada_counties_pop_2010 view
 
 SELECT *
 FROM nevada_counties_pop_2010
 LIMIT 5;
 
--- Listing 15-3: Creating a view showing population change for US counties
+-- Listing 17-3: Creating a view showing population change for US counties
 
 CREATE OR REPLACE VIEW county_pop_change_2010_2000 AS
     SELECT c2010.geo_name,
@@ -40,7 +40,7 @@ CREATE OR REPLACE VIEW county_pop_change_2010_2000 AS
        AND c2010.county_fips = c2000.county_fips
     ORDER BY c2010.state_fips, c2010.county_fips;
 
--- Listing 15-4: Selecting columns from the county_pop_change_2010_2000 view
+-- Listing 17-4: Selecting columns from the county_pop_change_2010_2000 view
 
 SELECT geo_name,
        st,
@@ -50,7 +50,7 @@ FROM county_pop_change_2010_2000
 WHERE st = 'NV'
 LIMIT 5;
 
--- Listing 15-5: Creating a view on the employees table
+-- Listing 17-5: Creating a view on the employees table
 
 CREATE OR REPLACE VIEW employees_tax_dept AS
      SELECT emp_id,
@@ -64,7 +64,7 @@ CREATE OR REPLACE VIEW employees_tax_dept AS
 
 SELECT * FROM employees_tax_dept;
 
--- Listing 15-6: Successful and rejected inserts via the employees_tax_dept view
+-- Listing 17-6: Successful and rejected inserts via the employees_tax_dept view
 
 INSERT INTO employees_tax_dept (first_name, last_name, dept_id)
 VALUES ('Suzanne', 'Legere', 1);
@@ -77,7 +77,7 @@ SELECT * FROM employees_tax_dept;
 
 SELECT * FROM employees;
 
--- Listing 15-7: Updating a row via the employees_tax_dept view
+-- Listing 17-7: Updating a row via the employees_tax_dept view
 
 UPDATE employees_tax_dept
 SET last_name = 'Le Gere'
@@ -90,7 +90,7 @@ UPDATE employees_tax_dept
 SET salary = 100000
 WHERE emp_id = 5;
 
--- Listing 15-8: Deleting a row via the employees_tax_dept view
+-- Listing 17-8: Deleting a row via the employees_tax_dept view
 
 DELETE FROM employees_tax_dept
 WHERE emp_id = 5;
@@ -99,7 +99,7 @@ WHERE emp_id = 5;
 -- FUNCTIONS
 -- https://www.postgresql.org/docs/current/static/plpgsql.html
 
--- Listing 15-9: Creating a percent_change function
+-- Listing 17-9: Creating a percent_change function
 -- To delete this function: DROP FUNCTION percent_change(numeric,numeric,integer);
 
 CREATE OR REPLACE FUNCTION
@@ -114,11 +114,11 @@ LANGUAGE SQL
 IMMUTABLE
 RETURNS NULL ON NULL INPUT;
 
--- Listing 15-10: Testing the percent_change() function
+-- Listing 17-10: Testing the percent_change() function
 
 SELECT percent_change(110, 108, 2);
 
--- Listing 15-11: Testing percent_change() on Census data
+-- Listing 17-11: Testing percent_change() on Census data
 
 SELECT c2010.geo_name,
        c2010.state_us_abbreviation AS st,
@@ -132,7 +132,7 @@ ON c2010.state_fips = c2000.state_fips
 ORDER BY pct_chg_func DESC
 LIMIT 5;
 
--- Listing 15-12: Adding a column to the teachers table and seeing the data
+-- Listing 17-12: Adding a column to the teachers table and seeing the data
 
 ALTER TABLE teachers ADD COLUMN personal_days integer;
 
@@ -142,7 +142,7 @@ SELECT first_name,
        personal_days
 FROM teachers;
 
--- Listing 15-13: Creating an update_personal_days() function
+-- Listing 17-13: Creating an update_personal_days() function
 
 CREATE OR REPLACE FUNCTION update_personal_days()
 RETURNS void AS $$
@@ -161,11 +161,11 @@ $$ LANGUAGE plpgsql;
 -- To run the function:
 SELECT update_personal_days();
 
--- Listing 15-14: Enabling the PL/Python procedural language
+-- Listing 17-14: Enabling the PL/Python procedural language
 
 CREATE EXTENSION plpythonu;
 
--- Listing 15-15: Using PL/Python to create the trim_county() function
+-- Listing 17-15: Using PL/Python to create the trim_county() function
 
 CREATE OR REPLACE FUNCTION trim_county(input_string text)
 RETURNS text AS $$
@@ -174,7 +174,7 @@ RETURNS text AS $$
     return cleaned
 $$ LANGUAGE plpythonu;
 
--- Listing 15-16: Testing the trim_county() function
+-- Listing 17-16: Testing the trim_county() function
 
 SELECT geo_name,
        trim_county(geo_name)
@@ -185,7 +185,7 @@ LIMIT 5;
 
 -- TRIGGERS
 
--- Listing 15-17: Creating the grades and grades_history tables
+-- Listing 17-17: Creating the grades and grades_history tables
 
 CREATE TABLE grades (
     student_id bigint,
@@ -212,7 +212,7 @@ CREATE TABLE grades_history (
 PRIMARY KEY (student_id, course_id, change_time)
 );  
 
--- Listing 15-18: Creating the record_if_grade_changed() function
+-- Listing 17-18: Creating the record_if_grade_changed() function
 
 CREATE OR REPLACE FUNCTION record_if_grade_changed()
     RETURNS trigger AS
@@ -238,7 +238,7 @@ BEGIN
 END;
 $$ LANGUAGE plpgsql;
 
--- Listing 15-19: Creating the grades_update trigger
+-- Listing 17-19: Creating the grades_update trigger
 
 CREATE TRIGGER grades_update
   AFTER UPDATE
@@ -246,7 +246,7 @@ CREATE TRIGGER grades_update
   FOR EACH ROW
   EXECUTE PROCEDURE record_if_grade_changed();
 
--- Listing 15-20: Testing the grades_update trigger
+-- Listing 17-20: Testing the grades_update trigger
 
 -- Initially, there should be 0 records in the history
 SELECT * FROM grades_history;
@@ -267,7 +267,7 @@ SELECT student_id,
        new_grade
 FROM grades_history;
 
--- Listing 15-21: Creating a temperature_test table
+-- Listing 17-21: Creating a temperature_test table
 
 CREATE TABLE temperature_test (
     station_name varchar(50),
@@ -278,7 +278,7 @@ CREATE TABLE temperature_test (
 PRIMARY KEY (station_name, observation_date)
 );
 
--- Listing 15-22: Creating the classify_max_temp() function
+-- Listing 17-22: Creating the classify_max_temp() function
 
 CREATE OR REPLACE FUNCTION classify_max_temp()
     RETURNS trigger AS
@@ -301,7 +301,7 @@ BEGIN
 END;
 $$ LANGUAGE plpgsql;
 
--- Listing 15-23: Creating the temperature_insert trigger
+-- Listing 17-23: Creating the temperature_insert trigger
 
 CREATE TRIGGER temperature_insert
     BEFORE INSERT
@@ -309,7 +309,7 @@ CREATE TRIGGER temperature_insert
     FOR EACH ROW
     EXECUTE PROCEDURE classify_max_temp();
 
--- Listing 15-24: Inserting rows to test the temperature_update trigger
+-- Listing 17-24: Inserting rows to test the temperature_update trigger
 
 INSERT INTO temperature_test (station_name, observation_date, max_temp, min_temp)
 VALUES

Chapter_18/psql_commands.txt

@@ -2,7 +2,7 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 16 Code Examples
+-- Chapter 18 Code Examples
 --------------------------------------------------------------
 
 
@@ -16,12 +16,12 @@ psql -d analysis -U postgres
 \c [database name] [user name]
 \c gis_analysis postgres
 
--- Listing 16-1: Entering a single-line query in psql
+-- Listing 18-1: Entering a single-line query in psql
 -- Enter this at the psql prompt:
 
 SELECT geo_name FROM us_counties_2010 LIMIT 3;
 
--- Listing 16-2: Entering a multi-line query in psql
+-- Listing 18-2: Entering a multi-line query in psql
 -- Type each line separately, followed by Enter
 
 SELECT geo_name
@@ -29,23 +29,23 @@ FROM us_counties_2010
 LIMIT 3;
 
 
--- Listing 16-3: Showing open parentheses in the psql prompt
+-- Listing 18-3: Showing open parentheses in the psql prompt
 
 CREATE TABLE wineries (
 id bigint,
 winery_name varchar(100)
 );
 
--- Listing 16-4: A query with scrolling results
+-- Listing 18-4: A query with scrolling results
 
 SELECT geo_name FROM us_counties_2010;
 
--- Listings 16-5 and 16-6: Normal and expanded displays of results
+-- Listings 18-5 and 18-6: Normal and expanded displays of results
 -- Use \x to toggle expanded on/off
 
 SELECT * FROM grades;
 
--- Listing 16-7: Importing data using \copy
+-- Listing 18-7: Importing data using \copy
 
 DROP TABLE state_regions;
 
@@ -56,7 +56,7 @@ CREATE TABLE state_regions (
 
 \copy state_regions FROM 'C:\YourDirectory\state_regions.csv' WITH (FORMAT CSV, HEADER);
 
--- Listing 16-8: Saving query output to a file
+-- Listing 18-8: Saving query output to a file
 
 -- Enter psql settings
 \a \f , \pset footer

Chapter_19/Chapter_19.sql

@@ -2,18 +2,18 @@
 -- Practical SQL: A Beginner's Guide to Storytelling with Data
 -- by Anthony DeBarros
 
--- Chapter 17 Code Examples
+-- Chapter 19 Code Examples
 --------------------------------------------------------------
 
 -- VACUUM
 
--- Listing 17-1: Creating a table to test vacuuming
+-- Listing 19-1: Creating a table to test vacuuming
 
 CREATE TABLE vacuum_test (
     integer_column integer
 );
 
--- Listing 17-2: Determining the size of vacuum_test
+-- Listing 19-2: Determining the size of vacuum_test
 
 SELECT pg_size_pretty(
            pg_total_relation_size('vacuum_test')
@@ -24,7 +24,7 @@ SELECT pg_size_pretty(
            pg_database_size('analysis')
        );
 
--- Listing 17-3: Inserting 500,000 rows into vacuum_test
+-- Listing 19-3: Inserting 500,000 rows into vacuum_test
 
 INSERT INTO vacuum_test
 SELECT * FROM generate_series(1,500000);
@@ -34,7 +34,7 @@ SELECT pg_size_pretty(
            pg_table_size('vacuum_test')
        );
 
--- Listing 17-4: Updating all rows in vacuum_test
+-- Listing 19-4: Updating all rows in vacuum_test
 
 UPDATE vacuum_test
 SET integer_column = integer_column + 1;
@@ -44,7 +44,7 @@ SELECT pg_size_pretty(
            pg_table_size('vacuum_test')
        );
 
--- Listing 17-5: Viewing autovacuum statistics for vacuum_test
+-- Listing 19-5: Viewing autovacuum statistics for vacuum_test
 
 SELECT relname,
        last_vacuum,
@@ -59,7 +59,7 @@ SELECT *
 FROM pg_stat_all_tables
 WHERE relname = 'vacuum_test';
 
--- Listing 17-6: Running VACUUM manually
+-- Listing 19-6: Running VACUUM manually
 
 VACUUM vacuum_test;
 
@@ -67,7 +67,7 @@ VACUUM; -- vacuums the whole database
 
 VACUUM VERBOSE; -- provides messages
 
--- Listing 17-7: Using VACUUM FULL to reclaim disk space
+-- Listing 19-7: Using VACUUM FULL to reclaim disk space
 
 VACUUM FULL vacuum_test;
 
@@ -78,11 +78,11 @@ SELECT pg_size_pretty(
        
 -- SETTINGS
 
--- Listing 17-8: Showing the location of postgresql.conf
+-- Listing 19-8: Showing the location of postgresql.conf
 
 SHOW config_file;
 
--- Listing 17-10: Show the location of the data directory
+-- Listing 19-10: Show the location of the data directory
 
 SHOW data_directory;
 
@@ -93,12 +93,12 @@ SHOW data_directory;
 
 -- BACKUP AND RESTORE
 
--- Listing 17-11: Backing up the analysis database with pg_dump
+-- Listing 19-11: Backing up the analysis database with pg_dump
 pg_dump -d analysis -U [user_name] -Fc > analysis_backup.sql
 
 -- Back up just a table
 pg_dump -t 'train_rides' -d analysis -U [user_name] -Fc > train_backup.sql 
 
--- Listing 17-12: Restoring the analysis database with pg_restore
+-- Listing 19-12: Restoring the analysis database with pg_restore
 
 pg_restore -C -d postgres -U postgres analysis_backup_custom.sql