Copy first edition files to second edition repo.

2020-02-24 21:39:06 -05:00 · 2020-02-24 21:39:06 -05:00 · 9552fdb805
commit 9552fdb805
parent 0c1ec7f4d0
60 changed files with 1356613 additions and 2 deletions
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,3 @@
+# General
+.DS_Store
+.vscode
--- a/Chapter_01/Chapter_01.sql
+++ b/Chapter_01/Chapter_01.sql
@ -0,0 +1,34 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 1 Code Examples
+--------------------------------------------------------------
+
+-- Listing 1-1: Creating a database named analysis
+
+CREATE DATABASE analysis;
+
+-- Listing 1-2: Creating a table named teachers with six columns
+
+CREATE TABLE teachers (
+    id bigserial,
+    first_name varchar(25),
+    last_name varchar(50),
+    school varchar(50),
+    hire_date date,
+    salary numeric
+);
+
+-- This command will remove (drop) the table.
+-- DROP TABLE teachers;
+
+-- Listing 1-3 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),
+       ('Lee', 'Reynolds', 'F.D. Roosevelt HS', '1993-05-22', 65000),
+       ('Samuel', 'Cole', 'Myers Middle School', '2005-08-01', 43500),
+       ('Samantha', 'Bush', 'Myers Middle School', '2011-10-30', 36200),
+       ('Betty', 'Diaz', 'Myers Middle School', '2005-08-30', 43500),
+       ('Kathleen', 'Roush', 'F.D. Roosevelt HS', '2010-10-22', 38500);
--- a/Chapter_01/README.md
+++ b/Chapter_01/README.md
@ -0,0 +1,8 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 1: Creating Your First Database and Table
+
+Introduces PostgreSQL, the pgAdmin user interface, and the code for loading a simple data set about teachers into a new database.
+
--- a/Chapter_02/Chapter_02.sql
+++ b/Chapter_02/Chapter_02.sql
@ -0,0 +1,104 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 2 Code Examples
+--------------------------------------------------------------
+
+-- Listing 2-1: Querying all rows and columns from the teachers table
+
+SELECT * FROM teachers;
+
+-- Listing 2-2: Querying a subset of columns
+
+SELECT last_name, first_name, salary FROM teachers;
+
+-- Listing 2-3: 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
+-- columns
+
+SELECT DISTINCT school, salary
+FROM teachers;
+
+-- Listing 2-5: 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
+
+SELECT last_name, school, hire_date
+FROM teachers
+ORDER BY school ASC, hire_date DESC;
+
+-- Listing 2-7: Filtering rows using WHERE
+
+SELECT last_name, school, hire_date
+FROM teachers
+WHERE school = 'Myers Middle School';
+
+-- Examples of WHERE comparison operators
+
+-- Teachers with first name of Janet
+SELECT first_name, last_name, school
+FROM teachers
+WHERE first_name = 'Janet';
+
+-- School names not equal to F.D. Roosevelt HS
+SELECT school
+FROM teachers
+WHERE school != 'F.D. Roosevelt HS';
+
+-- Teachers hired before Jan. 1, 2000
+SELECT first_name, last_name, hire_date
+FROM teachers
+WHERE hire_date < '2000-01-01';
+
+-- Teachers earning 43,500 or more
+SELECT first_name, last_name, salary
+FROM teachers
+WHERE salary >= 43500;
+
+-- Teachers who earn between $40,000 and $65,000
+SELECT first_name, last_name, school, salary
+FROM teachers
+WHERE salary BETWEEN 40000 AND 65000;
+
+-- Listing 2-8: Filtering with LIKE AND ILIKE
+
+SELECT first_name
+FROM teachers
+WHERE first_name LIKE 'sam%';
+
+SELECT first_name
+FROM teachers
+WHERE first_name ILIKE 'sam%';
+
+-- Listing 2-9: Combining operators using AND and OR
+
+SELECT *
+FROM teachers
+WHERE school = 'Myers Middle School'
+      AND salary < 40000;
+
+SELECT *
+FROM teachers
+WHERE last_name = 'Cole'
+      OR last_name = 'Bush';
+
+SELECT *
+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
+
+SELECT first_name, last_name, school, hire_date, salary
+FROM teachers
+WHERE school LIKE '%Roos%'
+ORDER BY hire_date DESC;
--- a/Chapter_02/README.md
+++ b/Chapter_02/README.md
@ -0,0 +1,10 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 2: Beginning Data Exploration with `SELECT`
+
+Explores basic SQL query syntax, including how to sort and filter data.
+
+
+
--- a/Chapter_03/Chapter_03.sql
+++ b/Chapter_03/Chapter_03.sql
@ -0,0 +1,90 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 3 Code Examples
+--------------------------------------------------------------
+
+-- Listing 3-1: Character data types in action
+
+CREATE TABLE char_data_types (
+    varchar_column varchar(10),
+    char_column char(10),
+    text_column text
+);
+
+INSERT INTO char_data_types
+VALUES
+    ('abc', 'abc', 'abc'),
+    ('defghi', 'defghi', 'defghi');
+
+COPY char_data_types TO 'C:\YourDirectory\typetest.txt'
+WITH (FORMAT CSV, HEADER, DELIMITER '|');
+
+
+-- Listing 3-2: Number data types in action
+
+CREATE TABLE number_data_types (
+    numeric_column numeric(20,5),
+    real_column real,
+    double_column double precision
+);
+
+INSERT INTO number_data_types
+VALUES
+    (.7, .7, .7),
+    (2.13579, 2.13579, 2.13579),
+    (2.1357987654, 2.1357987654, 2.1357987654);
+
+SELECT * FROM number_data_types;
+
+-- Listing 3-3: Rounding issues with float columns
+-- Assumes table created and loaded with Listing 3-2
+
+SELECT
+    numeric_column * 10000000 AS "Fixed",
+    real_column * 10000000 AS "Float"
+FROM number_data_types
+WHERE numeric_column = .7;
+
+-- Listing 3-4: Timestamp and interval types in action
+
+CREATE TABLE date_time_types (
+    timestamp_column timestamp with time zone,
+    interval_column interval
+);
+
+INSERT INTO date_time_types
+VALUES
+    ('2018-12-31 01:00 EST','2 days'),
+    ('2018-12-31 01:00 PST','1 month'),
+    ('2018-12-31 01:00 Australia/Melbourne','1 century'),
+    (now(),'1 week');
+
+SELECT * FROM date_time_types;
+
+-- Listing 3-5: Using the interval data type
+-- Assumes script 3-4 has been run
+
+SELECT
+    timestamp_column,
+    interval_column,
+    timestamp_column - interval_column AS new_date
+FROM date_time_types;
+
+-- Listing 3-6: Three CAST() examples
+
+SELECT timestamp_column, CAST(timestamp_column AS varchar(10))
+FROM date_time_types;
+
+SELECT numeric_column,
+       CAST(numeric_column AS integer),
+       CAST(numeric_column AS varchar(6))
+FROM number_data_types;
+
+-- Does not work:
+SELECT CAST(char_column AS integer) FROM char_data_types;
+
+-- Alternate notation for CAST is the double-colon:
+SELECT timestamp_column::varchar(10)
+FROM date_time_types;
--- a/Chapter_03/README.md
+++ b/Chapter_03/README.md
@ -0,0 +1,8 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 3: Understanding Data Types
+
+Explains the definitions for setting columns in a table to hold specific types of data, from text to dates to various forms of numbers.
+
--- a/Chapter_04/Chapter_04.sql
+++ b/Chapter_04/Chapter_04.sql
@ -0,0 +1,212 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 4 Code Examples
+--------------------------------------------------------------
+
+-- Listing 4-1: Using COPY for data import
+-- This is example syntax only; running it will produce an error
+
+COPY table_name
+FROM 'C:\YourDirectory\your_file.csv'
+WITH (FORMAT CSV, HEADER);
+
+
+-- Listing 4-2: 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
+
+CREATE TABLE us_counties_2010 (
+    geo_name varchar(90),                    -- Name of the geography
+    state_us_abbreviation varchar(2),        -- State/U.S. abbreviation
+    summary_level varchar(3),                -- Summary Level
+    region smallint,                         -- Region
+    division smallint,                       -- Division
+    state_fips varchar(2),                   -- State FIPS code
+    county_fips varchar(3),                  -- County code
+    area_land bigint,                        -- Area (Land) in square meters
+    area_water bigint,                        -- Area (Water) in square meters
+    population_count_100_percent integer,    -- Population count (100%)
+    housing_unit_count_100_percent integer,  -- Housing Unit count (100%)
+    internal_point_lat numeric(10,7),        -- Internal point (latitude)
+    internal_point_lon numeric(10,7),        -- Internal point (longitude)
+
+    -- This section is referred to as P1. Race:
+    p0010001 integer,   -- Total population
+    p0010002 integer,   -- Population of one race:
+    p0010003 integer,       -- White Alone
+    p0010004 integer,       -- Black or African American alone
+    p0010005 integer,       -- American Indian and Alaska Native alone
+    p0010006 integer,       -- Asian alone
+    p0010007 integer,       -- Native Hawaiian and Other Pacific Islander alone
+    p0010008 integer,       -- Some Other Race alone
+    p0010009 integer,   -- Population of two or more races
+    p0010010 integer,   -- Population of two races:
+    p0010011 integer,       -- White; Black or African American
+    p0010012 integer,       -- White; American Indian and Alaska Native
+    p0010013 integer,       -- White; Asian
+    p0010014 integer,       -- White; Native Hawaiian and Other Pacific Islander
+    p0010015 integer,       -- White; Some Other Race
+    p0010016 integer,       -- Black or African American; American Indian and Alaska Native
+    p0010017 integer,       -- Black or African American; Asian
+    p0010018 integer,       -- Black or African American; Native Hawaiian and Other Pacific Islander
+    p0010019 integer,       -- Black or African American; Some Other Race
+    p0010020 integer,       -- American Indian and Alaska Native; Asian
+    p0010021 integer,       -- American Indian and Alaska Native; Native Hawaiian and Other Pacific Islander
+    p0010022 integer,       -- American Indian and Alaska Native; Some Other Race
+    p0010023 integer,       -- Asian; Native Hawaiian and Other Pacific Islander
+    p0010024 integer,       -- Asian; Some Other Race
+    p0010025 integer,       -- Native Hawaiian and Other Pacific Islander; Some Other Race
+    p0010026 integer,   -- Population of three races
+    p0010047 integer,   -- Population of four races
+    p0010063 integer,   -- Population of five races
+    p0010070 integer,   -- Population of six races
+
+    -- This section is referred to as P2. HISPANIC OR LATINO, AND NOT HISPANIC OR LATINO BY RACE
+    p0020001 integer,   -- Total
+    p0020002 integer,   -- Hispanic or Latino
+    p0020003 integer,   -- Not Hispanic or Latino:
+    p0020004 integer,   -- Population of one race:
+    p0020005 integer,       -- White Alone
+    p0020006 integer,       -- Black or African American alone
+    p0020007 integer,       -- American Indian and Alaska Native alone
+    p0020008 integer,       -- Asian alone
+    p0020009 integer,       -- Native Hawaiian and Other Pacific Islander alone
+    p0020010 integer,       -- Some Other Race alone
+    p0020011 integer,   -- Two or More Races
+    p0020012 integer,   -- Population of two races
+    p0020028 integer,   -- Population of three races
+    p0020049 integer,   -- Population of four races
+    p0020065 integer,   -- Population of five races
+    p0020072 integer,   -- Population of six races
+
+    -- This section is referred to as P3. RACE FOR THE POPULATION 18 YEARS AND OVER
+    p0030001 integer,   -- Total
+    p0030002 integer,   -- Population of one race:
+    p0030003 integer,       -- White alone
+    p0030004 integer,       -- Black or African American alone
+    p0030005 integer,       -- American Indian and Alaska Native alone
+    p0030006 integer,       -- Asian alone
+    p0030007 integer,       -- Native Hawaiian and Other Pacific Islander alone
+    p0030008 integer,       -- Some Other Race alone
+    p0030009 integer,   -- Two or More Races
+    p0030010 integer,   -- Population of two races
+    p0030026 integer,   -- Population of three races
+    p0030047 integer,   -- Population of four races
+    p0030063 integer,   -- Population of five races
+    p0030070 integer,   -- Population of six races
+
+    -- This section is referred to as P4. HISPANIC OR LATINO, AND NOT HISPANIC OR LATINO BY RACE
+    -- FOR THE POPULATION 18 YEARS AND OVER
+    p0040001 integer,   -- Total
+    p0040002 integer,   -- Hispanic or Latino
+    p0040003 integer,   -- Not Hispanic or Latino:
+    p0040004 integer,   -- Population of one race:
+    p0040005 integer,   -- White alone
+    p0040006 integer,   -- Black or African American alone
+    p0040007 integer,   -- American Indian and Alaska Native alone
+    p0040008 integer,   -- Asian alone
+    p0040009 integer,   -- Native Hawaiian and Other Pacific Islander alone
+    p0040010 integer,   -- Some Other Race alone
+    p0040011 integer,   -- Two or More Races
+    p0040012 integer,   -- Population of two races
+    p0040028 integer,   -- Population of three races
+    p0040049 integer,   -- Population of four races
+    p0040065 integer,   -- Population of five races
+    p0040072 integer,   -- Population of six races
+
+    -- This section is referred to as H1. OCCUPANCY STATUS
+    h0010001 integer,   -- Total housing units
+    h0010002 integer,   -- Occupied
+    h0010003 integer    -- Vacant
+);
+
+SELECT * FROM us_counties_2010;
+
+-- Listing 4-3: 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.
+
+COPY us_counties_2010
+FROM 'C:\YourDirectory\us_counties_2010.csv'
+WITH (FORMAT CSV, HEADER);
+
+-- Checking the data
+
+SELECT * FROM us_counties_2010;
+
+SELECT geo_name, state_us_abbreviation, area_land
+FROM us_counties_2010
+ORDER BY area_land DESC
+LIMIT 3;
+
+SELECT geo_name, state_us_abbreviation, internal_point_lon
+FROM us_counties_2010
+ORDER BY internal_point_lon DESC
+LIMIT 5;
+
+
+-- Listing 4-4: Creating a table to track supervisor salaries
+
+CREATE TABLE supervisor_salaries (
+    town varchar(30),
+    county varchar(30),
+    supervisor varchar(30),
+    start_date date,
+    salary money,
+    benefits money
+);
+
+-- Listing 4-5: Importing salaries data from CSV to three table columns
+
+COPY supervisor_salaries (town, supervisor, salary)
+FROM 'C:\YourDirectory\supervisor_salaries.csv'
+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
+-- import
+
+DELETE FROM supervisor_salaries;
+
+CREATE TEMPORARY TABLE supervisor_salaries_temp (LIKE supervisor_salaries);
+
+COPY supervisor_salaries_temp (town, supervisor, salary)
+FROM 'C:\YourDirectory\supervisor_salaries.csv'
+WITH (FORMAT CSV, HEADER);
+
+INSERT INTO supervisor_salaries (town, county, supervisor, salary)
+SELECT town, 'Some County', supervisor, salary
+FROM supervisor_salaries_temp;
+
+DROP TABLE supervisor_salaries_temp;
+
+-- Check the data
+SELECT * FROM supervisor_salaries LIMIT 2;
+
+-- Listing 4-7: 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
+
+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
+
+COPY (
+    SELECT geo_name, state_us_abbreviation
+    FROM us_counties_2010
+    WHERE geo_name ILIKE '%mill%'
+     )
+TO 'C:\YourDirectory\us_counties_mill_export.txt'
+WITH (FORMAT CSV, HEADER, DELIMITER '|');
--- a/Chapter_04/README.md
+++ b/Chapter_04/README.md
@ -0,0 +1,7 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 4: Importing and Exporting Data
+
+Explains how to use SQL commands to load data from external files and then export it. You’ll load a table of U.S. Census population data that you’ll use throughout the book.
--- a/Chapter_04/supervisor_salaries.csv
+++ b/Chapter_04/supervisor_salaries.csv
@ -0,0 +1,6 @@
+town,supervisor,salary
+Anytown,Jones,27000
+Bumblyburg,Baker,24999
+Moetown,Smith,32100
+Bigville,Kao,31500
+New Brillig,Carroll,72690
--- a/Chapter_04/us_counties_2010.csv
+++ b/Chapter_04/us_counties_2010.csv
--- a/Chapter_05/Chapter_05.sql
+++ b/Chapter_05/Chapter_05.sql
@ -0,0 +1,207 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 5 Code Examples
+--------------------------------------------------------------
+
+-- Listing 5-1: 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
+
+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
+
+SELECT 3 ^ 4;    -- exponentiation
+SELECT |/ 10;    -- square root (operator)
+SELECT sqrt(10); -- square root (function)
+SELECT ||/ 10;   -- cube root
+SELECT 4 !;      -- factorial
+
+-- Order of operations
+
+SELECT 7 + 8 * 9; 	-- answer: 79
+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
+
+SELECT geo_name,
+       state_us_abbreviation AS "st",
+       p0010001 AS "Total Population",
+       p0010003 AS "White Alone",
+       p0010004 AS "Black or African American Alone",
+       p0010005 AS "Am Indian/Alaska Native Alone",
+       p0010006 AS "Asian Alone",
+       p0010007 AS "Native Hawaiian and Other Pacific Islander Alone",
+       p0010008 AS "Some Other Race Alone",
+       p0010009 AS "Two or More Races"
+FROM us_counties_2010;
+
+-- Listing 5-5: Adding two columns in us_counties_2010
+
+SELECT geo_name,
+       state_us_abbreviation AS "st",
+       p0010003 AS "White Alone",
+       p0010004 AS "Black Alone",
+       p0010003 + p0010004 AS "Total White and Black"
+FROM us_counties_2010;
+
+-- Listing 5-6: Checking Census data totals
+
+SELECT geo_name,
+       state_us_abbreviation AS "st",
+       p0010001 AS "Total",
+       p0010003 + p0010004 + p0010005 + p0010006 + p0010007
+           + p0010008 + p0010009 AS "All Races",
+       (p0010003 + p0010004 + p0010005 + p0010006 + p0010007
+           + p0010008 + p0010009) - p0010001 AS "Difference"
+FROM us_counties_2010
+ORDER BY "Difference" DESC;
+
+-- Listing 5-7: Calculating the percent of the population that is 
+-- Asian by county (percent of the whole)
+
+SELECT geo_name,
+       state_us_abbreviation AS "st",
+       (CAST(p0010006 AS numeric(8,1)) / p0010001) * 100 AS "pct_asian"
+FROM us_counties_2010
+ORDER BY "pct_asian" DESC;
+
+-- Listing 5-8: Calculating percent change
+
+CREATE TABLE percent_change (
+    department varchar(20),
+    spend_2014 numeric(10,2),
+    spend_2017 numeric(10,2)
+);
+
+INSERT INTO percent_change
+VALUES
+    ('Building', 250000, 289000),
+    ('Assessor', 178556, 179500),
+    ('Library', 87777, 90001),
+    ('Clerk', 451980, 650000),
+    ('Police', 250000, 223000),
+    ('Recreation', 199000, 195000);
+
+SELECT department,
+       spend_2014,
+       spend_2017,
+       round( (spend_2017 - spend_2014) /
+                    spend_2014 * 100, 1 ) AS "pct_change"
+FROM percent_change;
+
+-- Listing 5-9: 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
+
+CREATE TABLE percentile_test (
+    numbers integer
+);
+
+INSERT INTO percentile_test (numbers) VALUES
+    (1), (2), (3), (4), (5), (6);
+
+SELECT
+    percentile_cont(.5)
+    WITHIN GROUP (ORDER BY numbers),
+    percentile_disc(.5)
+    WITHIN GROUP (ORDER BY numbers)
+FROM percentile_test;
+
+-- Listing 5-11: Using sum(), avg(), and percentile_cont() aggregate functions
+
+SELECT sum(p0010001) AS "County Sum",
+       round(avg(p0010001), 0) AS "County Average",
+       percentile_cont(.5)
+       WITHIN GROUP (ORDER BY p0010001) AS "County Median"
+FROM us_counties_2010;
+
+-- Listing 5-12: Passing an array of values to percentile_cont()
+
+-- quartiles
+SELECT percentile_cont(array[.25,.5,.75])
+       WITHIN GROUP (ORDER BY p0010001) AS "quartiles"
+FROM us_counties_2010;
+
+-- Extra:
+-- quintiles
+SELECT percentile_cont(array[.2,.4,.6,.8])
+       WITHIN GROUP (ORDER BY p0010001) AS "quintiles"
+FROM us_counties_2010;
+
+-- deciles
+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
+
+SELECT unnest(
+            percentile_cont(array[.25,.5,.75])
+            WITHIN GROUP (ORDER BY p0010001)
+            ) AS "quartiles"
+FROM us_counties_2010;
+
+-- Listing 5-14: Creating a median() aggregate function in PostgreSQL
+-- Source: https://wiki.postgresql.org/wiki/Aggregate_Median
+
+CREATE OR REPLACE FUNCTION _final_median(anyarray)
+   RETURNS float8 AS
+$$
+  WITH q AS
+  (
+     SELECT val
+     FROM unnest($1) val
+     WHERE VAL IS NOT NULL
+     ORDER BY 1
+  ),
+  cnt AS
+  (
+    SELECT COUNT(*) AS c FROM q
+  )
+  SELECT AVG(val)::float8
+  FROM
+  (
+    SELECT val FROM q
+    LIMIT  2 - MOD((SELECT c FROM cnt), 2)
+    OFFSET GREATEST(CEIL((SELECT c FROM cnt) / 2.0) - 1,0)
+  ) q2;
+$$
+LANGUAGE sql IMMUTABLE;
+
+CREATE AGGREGATE median(anyelement) (
+  SFUNC=array_append,
+  STYPE=anyarray,
+  FINALFUNC=_final_median,
+  INITCOND='{}'
+);
+
+-- Listing 5-15: Using a median() aggregate function
+
+SELECT sum(p0010001) AS "County Sum",
+       round(avg(p0010001), 0) AS "County Average",
+       median(p0010001) AS "County Median",
+       percentile_cont(.5)
+       WITHIN GROUP (ORDER BY P0010001) AS "50th Percentile"
+FROM us_counties_2010;
+
+-- Listing 5-16: Finding the most-frequent value with mode()
+
+SELECT mode() WITHIN GROUP (ORDER BY p0010001)
+FROM us_counties_2010;
--- a/Chapter_05/README.md
+++ b/Chapter_05/README.md
@ -0,0 +1,10 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 5: Basic Math and Stats with SQL
+
+Covers arithmetic operations and introduces aggregate functions for finding sums, averages, and medians.
+
+
+
--- a/Chapter_06/Chapter_06.sql
+++ b/Chapter_06/Chapter_06.sql
@ -0,0 +1,196 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 6 Code Examples
+--------------------------------------------------------------
+
+-- Listing 6-1: Creating the departments and employees tables
+
+CREATE TABLE departments (
+    dept_id bigserial,
+    dept varchar(100),
+    city varchar(100),
+    CONSTRAINT dept_key PRIMARY KEY (dept_id),
+    CONSTRAINT dept_city_unique UNIQUE (dept, city)
+);
+
+CREATE TABLE employees (
+    emp_id bigserial,
+    first_name varchar(100),
+    last_name varchar(100),
+    salary integer,
+    dept_id integer REFERENCES departments (dept_id),
+    CONSTRAINT emp_key PRIMARY KEY (emp_id),
+    CONSTRAINT emp_dept_unique UNIQUE (emp_id, dept_id)
+);
+
+INSERT INTO departments (dept, city)
+VALUES
+    ('Tax', 'Atlanta'),
+    ('IT', 'Boston');
+
+INSERT INTO employees (first_name, last_name, salary, dept_id)
+VALUES
+    ('Nancy', 'Jones', 62500, 1),
+    ('Lee', 'Smith', 59300, 1),
+    ('Soo', 'Nguyen', 83000, 2),
+    ('Janet', 'King', 95000, 2);
+
+-- Listing 6-2: 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
+
+CREATE TABLE schools_left (
+    id integer CONSTRAINT left_id_key PRIMARY KEY,
+    left_school varchar(30)
+);
+
+CREATE TABLE schools_right (
+    id integer CONSTRAINT right_id_key PRIMARY KEY,
+    right_school varchar(30)
+);
+
+INSERT INTO schools_left (id, left_school) VALUES
+    (1, 'Oak Street School'),
+    (2, 'Roosevelt High School'),
+    (5, 'Washington Middle School'),
+    (6, 'Jefferson High School');
+
+INSERT INTO schools_right (id, right_school) VALUES
+    (1, 'Oak Street School'),
+    (2, 'Roosevelt High School'),
+    (3, 'Morrison Elementary'),
+    (4, 'Chase Magnet Academy'),
+    (6, 'Jefferson High School');
+
+-- Listing 6-4: Using JOIN
+
+SELECT *
+FROM schools_left JOIN schools_right
+ON schools_left.id = schools_right.id;
+
+-- Bonus: Also can be specified as INNER JOIN
+
+SELECT *
+FROM schools_left INNER JOIN schools_right
+ON schools_left.id = schools_right.id;
+
+-- Listing 6-5: Using LEFT JOIN
+
+SELECT *
+FROM schools_left LEFT JOIN schools_right
+ON schools_left.id = schools_right.id;
+
+-- Listing 6-6: Using RIGHT JOIN
+
+SELECT *
+FROM schools_left RIGHT JOIN schools_right
+ON schools_left.id = schools_right.id;
+
+-- Listing 6-7: 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
+
+SELECT *
+FROM schools_left CROSS JOIN schools_right;
+
+-- Listing 6-9: 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
+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
+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
+CREATE TABLE schools_enrollment (
+    id integer,
+    enrollment integer
+);
+
+CREATE TABLE schools_grades (
+    id integer,
+    grades varchar(10)
+);
+
+INSERT INTO schools_enrollment (id, enrollment)
+VALUES
+    (1, 360),
+    (2, 1001),
+    (5, 450),
+    (6, 927);
+
+INSERT INTO schools_grades (id, grades)
+VALUES
+    (1, 'K-3'),
+    (2, '9-12'),
+    (5, '6-8'),
+    (6, '9-12');
+
+SELECT lt.id, lt.left_school, en.enrollment, gr.grades
+FROM schools_left AS lt LEFT JOIN schools_enrollment AS en
+    ON lt.id = en.id
+LEFT JOIN schools_grades AS gr
+    ON lt.id = gr.id;
+
+-- Listing 6-13: 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
+
+CREATE TABLE us_counties_2000 (
+    geo_name varchar(90),              -- County/state name,
+    state_us_abbreviation varchar(2),  -- State/U.S. abbreviation
+    state_fips varchar(2),             -- State FIPS code
+    county_fips varchar(3),            -- County code
+    p0010001 integer,                  -- Total population
+    p0010002 integer,                  -- Population of one race:
+    p0010003 integer,                      -- White Alone
+    p0010004 integer,                      -- Black or African American alone
+    p0010005 integer,                      -- American Indian and Alaska Native alone
+    p0010006 integer,                      -- Asian alone
+    p0010007 integer,                      -- Native Hawaiian and Other Pacific Islander alone
+    p0010008 integer,                      -- Some Other Race alone
+    p0010009 integer,                  -- Population of two or more races
+    p0010010 integer,                  -- Population of two races
+    p0020002 integer,                  -- Hispanic or Latino
+    p0020003 integer                   -- Not Hispanic or Latino:
+);
+
+COPY us_counties_2000
+FROM 'C:\YourDirectory\us_counties_2000.csv'
+WITH (FORMAT CSV, HEADER);
+
+SELECT c2010.geo_name,
+       c2010.state_us_abbreviation AS state,
+       c2010.p0010001 AS pop_2010,
+       c2000.p0010001 AS pop_2000,
+       c2010.p0010001 - c2000.p0010001 AS raw_change,
+       round( (CAST(c2010.p0010001 AS numeric(8,1)) - c2000.p0010001)
+           / c2000.p0010001 * 100, 1 ) AS pct_change
+FROM us_counties_2010 c2010 INNER JOIN us_counties_2000 c2000
+ON c2010.state_fips = c2000.state_fips
+   AND c2010.county_fips = c2000.county_fips
+   AND c2010.p0010001 <> c2000.p0010001
+ORDER BY pct_change DESC;
--- a/Chapter_06/README.md
+++ b/Chapter_06/README.md
@ -0,0 +1,9 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 6: Joining Tables in a Relational Database
+
+Explains how to query multiple, related tables by joining them on key columns. You’ll learn how and when to use different types of joins.
+
+
--- a/Chapter_06/us_counties_2000.csv
+++ b/Chapter_06/us_counties_2000.csv
--- a/Chapter_07/Chapter_07.sql
+++ b/Chapter_07/Chapter_07.sql
@ -0,0 +1,184 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 7 Code Examples
+--------------------------------------------------------------
+
+-- Listing 7-1: Declaring a single-column natural key as primary key
+
+-- As a column constraint
+CREATE TABLE natural_key_example (
+    license_id varchar(10) CONSTRAINT license_key PRIMARY KEY,
+    first_name varchar(50),
+    last_name varchar(50)
+);
+
+-- Drop the table before trying again
+DROP TABLE natural_key_example;
+
+-- As a table constraint
+CREATE TABLE natural_key_example (
+    license_id varchar(10),
+    first_name varchar(50),
+    last_name varchar(50),
+    CONSTRAINT license_key PRIMARY KEY (license_id)
+);
+
+-- Listing 7-2: 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
+CREATE TABLE natural_key_composite_example (
+    student_id varchar(10),
+    school_day date,
+    present boolean,
+    CONSTRAINT student_key PRIMARY KEY (student_id, school_day)
+);
+
+-- Listing 7-4: Example of a composite primary key violation
+
+INSERT INTO natural_key_composite_example (student_id, school_day, present)
+VALUES(775, '1/22/2017', 'Y');
+
+INSERT INTO natural_key_composite_example (student_id, school_day, present)
+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
+
+CREATE TABLE surrogate_key_example (
+    order_number bigserial,
+    product_name varchar(50),
+    order_date date,
+    CONSTRAINT order_key PRIMARY KEY (order_number)
+);
+
+INSERT INTO surrogate_key_example (product_name, order_date)
+VALUES ('Beachball Polish', '2015-03-17'),
+       ('Wrinkle De-Atomizer', '2017-05-22'),
+       ('Flux Capacitor', '1985-10-26');
+
+SELECT * FROM surrogate_key_example;
+
+-- Listing 7-6: A foreign key example
+
+CREATE TABLE licenses (
+    license_id varchar(10),
+    first_name varchar(50),
+    last_name varchar(50),
+    CONSTRAINT licenses_key PRIMARY KEY (license_id)
+);
+
+CREATE TABLE registrations (
+    registration_id varchar(10),
+    registration_date date,
+    license_id varchar(10) REFERENCES licenses (license_id),
+    CONSTRAINT registration_key PRIMARY KEY (registration_id, license_id)
+);
+
+INSERT INTO licenses (license_id, first_name, last_name)
+VALUES ('T229901', 'Lynn', 'Malero');
+
+INSERT INTO registrations (registration_id, registration_date, license_id)
+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
+
+CREATE TABLE check_constraint_example (
+    user_id bigserial,
+    user_role varchar(50),
+    salary integer,
+    CONSTRAINT user_id_key PRIMARY KEY (user_id),
+    CONSTRAINT check_role_in_list CHECK (user_role IN('Admin', 'Staff')),
+    CONSTRAINT check_salary_not_zero CHECK (salary > 0)
+);
+
+-- Both of these will fail:
+INSERT INTO check_constraint_example (user_role)
+VALUES ('admin');
+
+INSERT INTO check_constraint_example (salary)
+VALUES (0);
+
+-- Listing 7-8: UNIQUE constraint example
+
+CREATE TABLE unique_constraint_example (
+    contact_id bigserial CONSTRAINT contact_id_key PRIMARY KEY,
+    first_name varchar(50),
+    last_name varchar(50),
+    email varchar(200),
+    CONSTRAINT email_unique UNIQUE (email)
+);
+
+INSERT INTO unique_constraint_example (first_name, last_name, email)
+VALUES ('Samantha', 'Lee', 'slee@example.org');
+
+INSERT INTO unique_constraint_example (first_name, last_name, email)
+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
+
+CREATE TABLE not_null_example (
+    student_id bigserial,
+    first_name varchar(50) NOT NULL,
+    last_name varchar(50) NOT NULL,
+    CONSTRAINT student_id_key PRIMARY KEY (student_id)
+);
+
+-- Listing 7-10: Dropping and adding a primary key and a NOT NULL constraint
+
+-- Drop
+ALTER TABLE not_null_example DROP CONSTRAINT student_id_key;
+
+-- Add
+ALTER TABLE not_null_example ADD CONSTRAINT student_id_key PRIMARY KEY (student_id);
+
+-- Drop
+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
+
+CREATE TABLE new_york_addresses (
+    longitude numeric(9,6),
+    latitude numeric(9,6),
+    street_number varchar(10),
+    street varchar(32),
+    unit varchar(7),
+    postcode varchar(5),
+    id integer CONSTRAINT new_york_key PRIMARY KEY
+);
+
+COPY new_york_addresses
+FROM 'C:\YourDirectory\city_of_new_york.csv'
+WITH (FORMAT CSV, HEADER);
+
+-- Listing 7-12: Benchmark queries for index performance
+
+EXPLAIN ANALYZE SELECT * FROM new_york_addresses
+WHERE street = 'BROADWAY';
+
+EXPLAIN ANALYZE SELECT * FROM new_york_addresses
+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
+
+CREATE INDEX street_idx ON new_york_addresses (street);
--- a/Chapter_07/README.md
+++ b/Chapter_07/README.md
@ -0,0 +1,11 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 7: Table Design that Works for You
+
+Covers how to set up tables to improve the organization and integrity of your data as well as how to speed up queries using indexes.
+
+
+
+
--- a/Chapter_07/city_of_new_york.csv
+++ b/Chapter_07/city_of_new_york.csv
--- a/Chapter_08/Chapter_08.sql
+++ b/Chapter_08/Chapter_08.sql
@ -0,0 +1,299 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 8 Code Examples
+--------------------------------------------------------------
+
+-- Listing 8-1: Creating and filling the 2014 Public Libraries Survey table
+
+CREATE TABLE pls_fy2014_pupld14a (
+    stabr varchar(2) NOT NULL,
+    fscskey varchar(6) CONSTRAINT fscskey2014_key PRIMARY KEY,
+    libid varchar(20) NOT NULL,
+    libname varchar(100) NOT NULL,
+    obereg varchar(2) NOT NULL,
+    rstatus integer NOT NULL,
+    statstru varchar(2) NOT NULL,
+    statname varchar(2) NOT NULL,
+    stataddr varchar(2) NOT NULL,
+    longitud numeric(10,7) NOT NULL,
+    latitude numeric(10,7) NOT NULL,
+    fipsst varchar(2) NOT NULL,
+    fipsco varchar(3) NOT NULL,
+    address varchar(35) NOT NULL,
+    city varchar(20) NOT NULL,
+    zip varchar(5) NOT NULL,
+    zip4 varchar(4) NOT NULL,
+    cnty varchar(20) NOT NULL,
+    phone varchar(10) NOT NULL,
+    c_relatn varchar(2) NOT NULL,
+    c_legbas varchar(2) NOT NULL,
+    c_admin varchar(2) NOT NULL,
+    geocode varchar(3) NOT NULL,
+    lsabound varchar(1) NOT NULL,
+    startdat varchar(10),
+    enddate varchar(10),
+    popu_lsa integer NOT NULL,
+    centlib integer NOT NULL,
+    branlib integer NOT NULL,
+    bkmob integer NOT NULL,
+    master numeric(8,2) NOT NULL,
+    libraria numeric(8,2) NOT NULL,
+    totstaff numeric(8,2) NOT NULL,
+    locgvt integer NOT NULL,
+    stgvt integer NOT NULL,
+    fedgvt integer NOT NULL,
+    totincm integer NOT NULL,
+    salaries integer,
+    benefit integer,
+    staffexp integer,
+    prmatexp integer NOT NULL,
+    elmatexp integer NOT NULL,
+    totexpco integer NOT NULL,
+    totopexp integer NOT NULL,
+    lcap_rev integer NOT NULL,
+    scap_rev integer NOT NULL,
+    fcap_rev integer NOT NULL,
+    cap_rev integer NOT NULL,
+    capital integer NOT NULL,
+    bkvol integer NOT NULL,
+    ebook integer NOT NULL,
+    audio_ph integer NOT NULL,
+    audio_dl float NOT NULL,
+    video_ph integer NOT NULL,
+    video_dl float NOT NULL,
+    databases integer NOT NULL,
+    subscrip integer NOT NULL,
+    hrs_open integer NOT NULL,
+    visits integer NOT NULL,
+    referenc integer NOT NULL,
+    regbor integer NOT NULL,
+    totcir integer NOT NULL,
+    kidcircl integer NOT NULL,
+    elmatcir integer NOT NULL,
+    loanto integer NOT NULL,
+    loanfm integer NOT NULL,
+    totpro integer NOT NULL,
+    totatten integer NOT NULL,
+    gpterms integer NOT NULL,
+    pitusr integer NOT NULL,
+    wifisess integer NOT NULL,
+    yr_sub integer NOT NULL
+);
+
+CREATE INDEX libname2014_idx ON pls_fy2014_pupld14a (libname);
+CREATE INDEX stabr2014_idx ON pls_fy2014_pupld14a (stabr);
+CREATE INDEX city2014_idx ON pls_fy2014_pupld14a (city);
+CREATE INDEX visits2014_idx ON pls_fy2014_pupld14a (visits);
+
+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
+
+CREATE TABLE pls_fy2009_pupld09a (
+    stabr varchar(2) NOT NULL,
+    fscskey varchar(6) CONSTRAINT fscskey2009_key PRIMARY KEY,
+    libid varchar(20) NOT NULL,
+    libname varchar(100) NOT NULL,
+    address varchar(35) NOT NULL,
+    city varchar(20) NOT NULL,
+    zip varchar(5) NOT NULL,
+    zip4 varchar(4) NOT NULL,
+    cnty varchar(20) NOT NULL,
+    phone varchar(10) NOT NULL,
+    c_relatn varchar(2) NOT NULL,
+    c_legbas varchar(2) NOT NULL,
+    c_admin varchar(2) NOT NULL,
+    geocode varchar(3) NOT NULL,
+    lsabound varchar(1) NOT NULL,
+    startdat varchar(10),
+    enddate varchar(10),
+    popu_lsa integer NOT NULL,
+    centlib integer NOT NULL,
+    branlib integer NOT NULL,
+    bkmob integer NOT NULL,
+    master numeric(8,2) NOT NULL,
+    libraria numeric(8,2) NOT NULL,
+    totstaff numeric(8,2) NOT NULL,
+    locgvt integer NOT NULL,
+    stgvt integer NOT NULL,
+    fedgvt integer NOT NULL,
+    totincm integer NOT NULL,
+    salaries integer,
+    benefit integer,
+    staffexp integer,
+    prmatexp integer NOT NULL,
+    elmatexp integer NOT NULL,
+    totexpco integer NOT NULL,
+    totopexp integer NOT NULL,
+    lcap_rev integer NOT NULL,
+    scap_rev integer NOT NULL,
+    fcap_rev integer NOT NULL,
+    cap_rev integer NOT NULL,
+    capital integer NOT NULL,
+    bkvol integer NOT NULL,
+    ebook integer NOT NULL,
+    audio integer NOT NULL,
+    video integer NOT NULL,
+    databases integer NOT NULL,
+    subscrip integer NOT NULL,
+    hrs_open integer NOT NULL,
+    visits integer NOT NULL,
+    referenc integer NOT NULL,
+    regbor integer NOT NULL,
+    totcir integer NOT NULL,
+    kidcircl integer NOT NULL,
+    loanto integer NOT NULL,
+    loanfm integer NOT NULL,
+    totpro integer NOT NULL,
+    totatten integer NOT NULL,
+    gpterms integer NOT NULL,
+    pitusr integer NOT NULL,
+    yr_sub integer NOT NULL,
+    obereg varchar(2) NOT NULL,
+    rstatus integer NOT NULL,
+    statstru varchar(2) NOT NULL,
+    statname varchar(2) NOT NULL,
+    stataddr varchar(2) NOT NULL,
+    longitud numeric(10,7) NOT NULL,
+    latitude numeric(10,7) NOT NULL,
+    fipsst varchar(2) NOT NULL,
+    fipsco varchar(3) NOT NULL
+);
+
+CREATE INDEX libname2009_idx ON pls_fy2009_pupld09a (libname);
+CREATE INDEX stabr2009_idx ON pls_fy2009_pupld09a (stabr);
+CREATE INDEX city2009_idx ON pls_fy2009_pupld09a (city);
+CREATE INDEX visits2009_idx ON pls_fy2009_pupld09a (visits);
+
+COPY pls_fy2009_pupld09a
+FROM 'C:\YourDirectory\pls_fy2009_pupld09a.csv'
+WITH (FORMAT CSV, HEADER);
+
+-- Listing 8-3: Using count() for table row counts
+
+SELECT count(*)
+FROM pls_fy2014_pupld14a;
+
+SELECT count(*)
+FROM pls_fy2009_pupld09a;
+
+-- Listing 8-4: 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
+
+SELECT count(libname)
+FROM pls_fy2014_pupld14a;
+
+SELECT count(DISTINCT libname)
+FROM pls_fy2014_pupld14a;
+
+-- Bonus: find duplicate libnames
+SELECT libname, count(libname)
+FROM pls_fy2014_pupld14a
+GROUP BY libname
+ORDER BY count(libname) DESC;
+
+-- Bonus: see location of every Oxford Public Library
+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()
+SELECT max(visits), min(visits)
+FROM pls_fy2014_pupld14a;
+
+-- Listing 8-7: Using GROUP BY on the stabr column
+
+-- There are 56 in 2014.
+SELECT stabr
+FROM pls_fy2014_pupld14a
+GROUP BY stabr
+ORDER BY stabr;
+
+-- Bonus: there are 55 in 2009.
+SELECT stabr
+FROM pls_fy2009_pupld09a
+GROUP BY stabr
+ORDER BY stabr;
+
+-- Listing 8-8: Using GROUP BY on the city and stabr columns
+
+SELECT city, stabr
+FROM pls_fy2014_pupld14a
+GROUP BY city, stabr
+ORDER BY city, stabr;
+
+-- Bonus: We can count some of the combos
+SELECT city, stabr, count(*)
+FROM pls_fy2014_pupld14a
+GROUP BY city, stabr
+ORDER BY count(*) DESC;
+
+-- Listing 8-9: 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
+
+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
+-- libraries in 2014 and 2009
+
+-- 2014
+SELECT sum(visits) AS visits_2014
+FROM pls_fy2014_pupld14a
+WHERE visits >= 0;
+
+-- 2009
+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
+
+SELECT sum(pls14.visits) AS visits_2014,
+       sum(pls09.visits) AS visits_2009
+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
+
+SELECT pls14.stabr,
+       sum(pls14.visits) AS visits_2014,
+       sum(pls09.visits) AS visits_2009,
+       round( (CAST(sum(pls14.visits) AS decimal(10,1)) - sum(pls09.visits)) /
+                    sum(pls09.visits) * 100, 2 ) AS pct_change
+FROM pls_fy2014_pupld14a pls14 JOIN pls_fy2009_pupld09a pls09
+ON pls14.fscskey = pls09.fscskey
+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
+
+SELECT pls14.stabr,
+       sum(pls14.visits) AS visits_2014,
+       sum(pls09.visits) AS visits_2009,
+       round( (CAST(sum(pls14.visits) AS decimal(10,1)) - sum(pls09.visits)) /
+                    sum(pls09.visits) * 100, 2 ) AS pct_change
+FROM pls_fy2014_pupld14a pls14 JOIN pls_fy2009_pupld09a pls09
+ON pls14.fscskey = pls09.fscskey
+WHERE pls14.visits >= 0 AND pls09.visits >= 0
+GROUP BY pls14.stabr
+HAVING sum(pls14.visits) > 50000000
+ORDER BY pct_change DESC;
--- a/Chapter_08/README.md
+++ b/Chapter_08/README.md
@ -0,0 +1,10 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 8: Extracting Information by Grouping and Summarizing
+
+Explains how to use aggregate functions to find trends in U.S. library use based on annual surveys.
+
+
+
--- a/Chapter_08/pls_fy2009_pupld09a.csv
+++ b/Chapter_08/pls_fy2009_pupld09a.csv
--- a/Chapter_08/pls_fy2014_pupld14a.csv
+++ b/Chapter_08/pls_fy2014_pupld14a.csv
--- a/Chapter_09/Chapter_09.sql
+++ b/Chapter_09/Chapter_09.sql
@ -0,0 +1,259 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 9 Code Examples
+--------------------------------------------------------------
+
+-- Listing 9-1: 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 (
+    est_number varchar(50) CONSTRAINT est_number_key PRIMARY KEY,
+    company varchar(100),
+    street varchar(100),
+    city varchar(30),
+    st varchar(2),
+    zip varchar(5),
+    phone varchar(14),
+    grant_date date,
+    activities text,
+    dbas text
+);
+
+COPY meat_poultry_egg_inspect
+FROM 'C:\YourDirectory\MPI_Directory_by_Establishment_Name.csv'
+WITH (FORMAT CSV, HEADER, DELIMITER ',');
+
+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
+SELECT company,
+       street,
+       city,
+       st,
+       count(*) AS address_count
+FROM meat_poultry_egg_inspect
+GROUP BY company, street, city, st
+HAVING count(*) > 1
+ORDER BY company, street, city, st;
+
+-- Listing 9-3: 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
+SELECT est_number,
+       company,
+       city,
+       st,
+       zip
+FROM meat_poultry_egg_inspect
+WHERE st IS NULL;
+
+-- Listing 9-5: Using GROUP BY and count() to find inconsistent company names
+
+SELECT company,
+       count(*) AS company_count
+FROM meat_poultry_egg_inspect
+GROUP BY company
+ORDER BY company ASC;
+
+-- Listing 9-6: Using length() and count() to test the zip column
+
+SELECT length(zip),
+       count(*) AS length_count
+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
+
+SELECT st,
+       count(*) AS st_count
+FROM meat_poultry_egg_inspect
+WHERE length(zip) < 5
+GROUP BY st
+ORDER BY st ASC;
+
+-- Listing 9-8: Backing up a table
+
+CREATE TABLE meat_poultry_egg_inspect_backup AS
+SELECT * FROM meat_poultry_egg_inspect;
+
+-- Check number of records:
+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
+
+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
+
+SELECT st,
+       st_copy
+FROM meat_poultry_egg_inspect
+ORDER BY st;
+
+-- Listing 9-11: Updating the st column for three establishments
+
+UPDATE meat_poultry_egg_inspect
+SET st = 'MN'
+WHERE est_number = 'V18677A';
+
+UPDATE meat_poultry_egg_inspect
+SET st = 'AL'
+WHERE est_number = 'M45319+P45319';
+
+UPDATE meat_poultry_egg_inspect
+SET st = 'WI'
+WHERE est_number = 'M263A+P263A+V263A';
+
+-- Listing 9-12: Restoring original st column values
+
+-- Restoring from the column backup
+UPDATE meat_poultry_egg_inspect
+SET st = st_copy;
+
+-- Restoring from the table backup
+UPDATE meat_poultry_egg_inspect original
+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
+
+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
+
+UPDATE meat_poultry_egg_inspect
+SET company_standard = 'Armour-Eckrich Meats'
+WHERE company LIKE 'Armour%';
+
+SELECT company, company_standard
+FROM meat_poultry_egg_inspect
+WHERE company LIKE 'Armour%';
+
+-- Listing 9-15: 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
+
+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
+
+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
+
+CREATE TABLE state_regions (
+    st varchar(2) CONSTRAINT st_key PRIMARY KEY,
+    region varchar(20) NOT NULL
+);
+
+COPY state_regions
+FROM 'C:\YourDirectory\state_regions.csv'
+WITH (FORMAT CSV, HEADER, DELIMITER ',');
+
+-- Listing 9-19: 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'
+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
+
+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
+
+DELETE FROM meat_poultry_egg_inspect
+WHERE st IN('PR','VI');
+
+-- Listing 9-22: 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
+
+DROP TABLE meat_poultry_egg_inspect_backup;
+
+-- Listing 9-24: Demonstrating a transaction block
+
+-- Start transaction and perform update
+START TRANSACTION;
+
+UPDATE meat_poultry_egg_inspect
+SET company = 'AGRO Merchantss Oakland LLC'
+WHERE company = 'AGRO Merchants Oakland, LLC';
+
+-- view changes
+SELECT company
+FROM meat_poultry_egg_inspect
+WHERE company LIKE 'AGRO%'
+ORDER BY company;
+
+-- Revert changes
+ROLLBACK;
+
+-- See restored state
+SELECT company
+FROM meat_poultry_egg_inspect
+WHERE company LIKE 'AGRO%'
+ORDER BY company;
+
+-- Alternately, commit changes at the end:
+START TRANSACTION;
+
+UPDATE meat_poultry_egg_inspect
+SET company = 'AGRO Merchants Oakland LLC'
+WHERE company = 'AGRO Merchants Oakland, LLC';
+
+COMMIT;
+
+-- Listing 9-25: 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
+
+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;
+ALTER TABLE meat_poultry_egg_inspect_temp RENAME TO meat_poultry_egg_inspect_backup;
+
+
--- a/Chapter_09/MPI_Directory_by_Establishment_Name.csv
+++ b/Chapter_09/MPI_Directory_by_Establishment_Name.csv
--- a/Chapter_09/README.md
+++ b/Chapter_09/README.md
@ -0,0 +1,8 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 9: Inspecting and Modifying Data
+
+Explores how to  nd and  x incomplete or inaccurate data using a collection of records about meat, egg, and poultry producers as an example.
+
--- a/Chapter_09/state_regions.csv
+++ b/Chapter_09/state_regions.csv
@ -0,0 +1,57 @@
+st,region
+AK,Pacific
+AL,East South Central
+AR,West South Central
+AS,Outlying Area
+AZ,Mountain
+CA,Pacific
+CO,Mountain
+CT,New England
+DC,South Atlantic
+DE,South Atlantic
+FL,South Atlantic
+GA,South Atlantic
+GU,Outlying Area
+HI,Pacific
+IA,West North Central
+ID,Mountain
+IL,East North Central
+IN,East North Central
+KS,West North Central
+KY,East South Central
+LA,West South Central
+MA,New England
+MD,South Atlantic
+ME,New England
+MI,East North Central
+MN,West North Central
+MO,West North Central
+MP,Outlying Area
+MS,East South Central
+MT,Mountain
+NC,South Atlantic
+ND,West North Central
+NE,West North Central
+NH,New England
+NJ,Middle Atlantic
+NM,Mountain
+NV,Mountain
+NY,Middle Atlantic
+OH,East North Central
+OK,West South Central
+OR,Pacific
+PA,Middle Atlantic
+PR,Outlying Area
+RI,New England
+SC,South Atlantic
+SD,West North Central
+TN,East South Central
+TX,West South Central
+UT,Mountain
+VA,South Atlantic
+VI,Outlying Area
+VT,New England
+WA,Pacific
+WI,East North Central
+WV,South Atlantic
+WY,Mountain
--- a/Chapter_10/Chapter_10.sql
+++ b/Chapter_10/Chapter_10.sql
@ -0,0 +1,167 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 10 Code Examples
+--------------------------------------------------------------
+
+-- Listing 10-1: 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,
+    county varchar(50) NOT NULL,
+    st varchar(20) NOT NULL,
+    pct_travel_60_min numeric(5,3) NOT NULL,
+    pct_bachelors_higher numeric(5,3) NOT NULL,
+    pct_masters_higher numeric(5,3) NOT NULL,
+    median_hh_income integer,
+    CHECK (pct_masters_higher <= pct_bachelors_higher)
+);
+
+COPY acs_2011_2015_stats
+FROM 'C:\YourDirectory\acs_2011_2015_stats.csv'
+WITH (FORMAT CSV, HEADER, DELIMITER ',');
+
+SELECT * FROM acs_2011_2015_stats;
+
+-- Listing 10-2: 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
+
+SELECT
+    round(
+      corr(median_hh_income, pct_bachelors_higher)::numeric, 2
+      ) AS bachelors_income_r,
+    round(
+      corr(pct_travel_60_min, median_hh_income)::numeric, 2
+      ) AS income_travel_r,
+    round(
+      corr(pct_travel_60_min, pct_bachelors_higher)::numeric, 2
+      ) AS bachelors_travel_r
+FROM acs_2011_2015_stats;
+
+-- Listing 10-4: Regression slope and intercept functions
+
+SELECT
+    round(
+        regr_slope(median_hh_income, pct_bachelors_higher)::numeric, 2
+        ) AS slope,
+    round(
+        regr_intercept(median_hh_income, pct_bachelors_higher)::numeric, 2
+        ) AS y_intercept
+FROM acs_2011_2015_stats;
+
+-- Listing 10-5: Calculating the coefficient of determination, or r-squared
+
+SELECT round(
+        regr_r2(median_hh_income, pct_bachelors_higher)::numeric, 3
+        ) AS r_squared
+FROM acs_2011_2015_stats;
+
+-- Bonus: Additional stats functions.
+-- Variance
+SELECT var_pop(median_hh_income)
+FROM acs_2011_2015_stats;
+
+-- Standard deviation of the entire population
+SELECT stddev_pop(median_hh_income)
+FROM acs_2011_2015_stats;
+
+-- Covariance
+SELECT covar_pop(median_hh_income, pct_bachelors_higher)
+FROM acs_2011_2015_stats;
+
+-- Listing 10-6: The rank() and dense_rank() window functions
+
+CREATE TABLE widget_companies (
+    id bigserial,
+    company varchar(30) NOT NULL,
+    widget_output integer NOT NULL
+);
+
+INSERT INTO widget_companies (company, widget_output)
+VALUES
+    ('Morse Widgets', 125000),
+    ('Springfield Widget Masters', 143000),
+    ('Best Widgets', 196000),
+    ('Acme Inc.', 133000),
+    ('District Widget Inc.', 201000),
+    ('Clarke Amalgamated', 620000),
+    ('Stavesacre Industries', 244000),
+    ('Bowers Widget Emporium', 201000);
+
+SELECT
+    company,
+    widget_output,
+    rank() OVER (ORDER BY widget_output DESC),
+    dense_rank() OVER (ORDER BY widget_output DESC)
+FROM widget_companies;
+
+-- Listing 10-7: Applying rank() within groups using PARTITION BY
+
+CREATE TABLE store_sales (
+    store varchar(30),
+    category varchar(30) NOT NULL,
+    unit_sales bigint NOT NULL,
+    CONSTRAINT store_category_key PRIMARY KEY (store, category)
+);
+
+INSERT INTO store_sales (store, category, unit_sales)
+VALUES
+    ('Broders', 'Cereal', 1104),
+    ('Wallace', 'Ice Cream', 1863),
+    ('Broders', 'Ice Cream', 2517),
+    ('Cramers', 'Ice Cream', 2112),
+    ('Broders', 'Beer', 641),
+    ('Cramers', 'Cereal', 1003),
+    ('Cramers', 'Beer', 640),
+    ('Wallace', 'Cereal', 980),
+    ('Wallace', 'Beer', 988);
+
+SELECT
+    category,
+    store,
+    unit_sales,
+    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
+
+CREATE TABLE fbi_crime_data_2015 (
+    st varchar(20),
+    city varchar(50),
+    population integer,
+    violent_crime integer,
+    property_crime integer,
+    burglary integer,
+    larceny_theft integer,
+    motor_vehicle_theft integer,
+    CONSTRAINT st_city_key PRIMARY KEY (st, city)
+);
+
+COPY fbi_crime_data_2015
+FROM 'C:\YourDirectory\fbi_crime_data_2015.csv'
+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
+-- or more people
+
+SELECT
+    city,
+    st,
+    population,
+    property_crime,
+    round(
+        (property_crime::numeric / population) * 1000, 1
+        ) AS pc_per_1000
+FROM fbi_crime_data_2015
+WHERE population >= 500000
+ORDER BY (property_crime::numeric / population) DESC;
--- a/Chapter_10/README.md
+++ b/Chapter_10/README.md
@ -0,0 +1,8 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 10: Statistical Functions in SQL
+
+Introduces correlation, regression, and ranking functions in SQL to help you derive more meaning from data sets.
+
--- a/Chapter_10/acs_2011_2015_stats.csv
+++ b/Chapter_10/acs_2011_2015_stats.csv
--- a/Chapter_10/fbi_crime_data_2015.csv
+++ b/Chapter_10/fbi_crime_data_2015.csv
--- a/Chapter_11/Chapter_11.sql
+++ b/Chapter_11/Chapter_11.sql
@ -0,0 +1,233 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 11 Code Examples
+--------------------------------------------------------------
+
+-- Listing 11-1: Extracting components of a timestamp value using date_part()
+
+SELECT
+    date_part('year', '2019-12-01 18:37:12 EST'::timestamptz) AS "year",
+    date_part('month', '2019-12-01 18:37:12 EST'::timestamptz) AS "month",
+    date_part('day', '2019-12-01 18:37:12 EST'::timestamptz) AS "day",
+    date_part('hour', '2019-12-01 18:37:12 EST'::timestamptz) AS "hour",
+    date_part('minute', '2019-12-01 18:37:12 EST'::timestamptz) AS "minute",
+    date_part('seconds', '2019-12-01 18:37:12 EST'::timestamptz) AS "seconds",
+    date_part('timezone_hour', '2019-12-01 18:37:12 EST'::timestamptz) AS "tz",
+    date_part('week', '2019-12-01 18:37:12 EST'::timestamptz) AS "week",
+    date_part('quarter', '2019-12-01 18:37:12 EST'::timestamptz) AS "quarter",
+    date_part('epoch', '2019-12-01 18:37:12 EST'::timestamptz) AS "epoch";
+
+-- Bonus: Using the SQL-standard extract() for similar datetime parsing:
+
+SELECT extract('year' from '2019-12-01 18:37:12 EST'::timestamptz) AS "year";
+
+-- Listing 11-2: Three functions for making datetimes from components
+
+-- make a date
+SELECT make_date(2018, 2, 22);
+-- make a time
+SELECT make_time(18, 4, 30.3);
+-- make a timestamp with time zone
+SELECT make_timestamptz(2018, 2, 22, 18, 4, 30.3, 'Europe/Lisbon');
+
+-- Bonus: Retrieving the current date and time
+
+SELECT
+    current_date,
+    current_time,
+    current_timestamp,
+    localtime,
+    localtimestamp,
+    now();
+
+-- Listing 11-3: Comparing current_timestamp and clock_timestamp() during row insert
+
+CREATE TABLE current_time_example (
+    time_id bigserial,
+    current_timestamp_col timestamp with time zone,
+    clock_timestamp_col timestamp with time zone
+);
+
+INSERT INTO current_time_example (current_timestamp_col, clock_timestamp_col)
+    (SELECT current_timestamp,
+            clock_timestamp()
+     FROM generate_series(1,1000));
+
+SELECT * FROM current_time_example;
+
+-- Time Zones
+
+-- Listing 11-4: 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
+
+SELECT * FROM pg_timezone_abbrevs;
+SELECT * FROM pg_timezone_names;
+
+-- Filter to find one
+SELECT * FROM pg_timezone_names
+WHERE name LIKE 'Europe%';
+
+-- Listing 11-6: Setting the time zone for a client session
+
+SET timezone TO 'US/Pacific';
+
+CREATE TABLE time_zone_test (
+    test_date timestamp with time zone
+);
+INSERT INTO time_zone_test VALUES ('2020-01-01 4:00');
+
+SELECT test_date
+FROM time_zone_test;
+
+SET timezone TO 'US/Eastern';
+
+SELECT test_date
+FROM time_zone_test;
+
+SELECT test_date AT TIME ZONE 'Asia/Seoul'
+FROM time_zone_test;
+
+
+-- Math with dates!
+
+SELECT '9/30/1929'::date - '9/27/1929'::date;
+SELECT '9/30/1929'::date + '5 years'::interval;
+
+
+-- Taxi Rides
+
+-- Listing 11-7: Creating a table and importing NYC yellow taxi data
+
+CREATE TABLE nyc_yellow_taxi_trips_2016_06_01 (
+    trip_id bigserial PRIMARY KEY,
+    vendor_id varchar(1) NOT NULL,
+    tpep_pickup_datetime timestamp with time zone NOT NULL,
+    tpep_dropoff_datetime timestamp with time zone NOT NULL,
+    passenger_count integer NOT NULL,
+    trip_distance numeric(8,2) NOT NULL,
+    pickup_longitude numeric(18,15) NOT NULL,
+    pickup_latitude numeric(18,15) NOT NULL,
+    rate_code_id varchar(2) NOT NULL,
+    store_and_fwd_flag varchar(1) NOT NULL,
+    dropoff_longitude numeric(18,15) NOT NULL,
+    dropoff_latitude numeric(18,15) NOT NULL,
+    payment_type varchar(1) NOT NULL,
+    fare_amount numeric(9,2) NOT NULL,
+    extra numeric(9,2) NOT NULL,
+    mta_tax numeric(5,2) NOT NULL,
+    tip_amount numeric(9,2) NOT NULL,
+    tolls_amount numeric(9,2) NOT NULL,
+    improvement_surcharge numeric(9,2) NOT NULL,
+    total_amount numeric(9,2) NOT NULL
+);
+
+COPY nyc_yellow_taxi_trips_2016_06_01 (
+    vendor_id,
+    tpep_pickup_datetime,
+    tpep_dropoff_datetime,
+    passenger_count,
+    trip_distance,
+    pickup_longitude,
+    pickup_latitude,
+    rate_code_id,
+    store_and_fwd_flag,
+    dropoff_longitude,
+    dropoff_latitude,
+    payment_type,
+    fare_amount,
+    extra,
+    mta_tax,
+    tip_amount,
+    tolls_amount,
+    improvement_surcharge,
+    total_amount
+   )
+FROM 'C:\YourDirectory\yellow_tripdata_2016_06_01.csv'
+WITH (FORMAT CSV, HEADER, DELIMITER ',');
+
+CREATE INDEX tpep_pickup_idx
+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
+
+SELECT
+    date_part('hour', tpep_pickup_datetime) AS trip_hour,
+    count(*)
+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
+
+COPY
+    (SELECT
+        date_part('hour', tpep_pickup_datetime) AS trip_hour,
+        count(*)
+    FROM nyc_yellow_taxi_trips_2016_06_01
+    GROUP BY trip_hour
+    ORDER BY trip_hour
+    )
+TO 'C:\YourDirectory\hourly_pickups_2016_06_01.csv'
+WITH (FORMAT CSV, HEADER, DELIMITER ',');
+
+-- Listing 11-10: Calculating median trip time by hour
+
+SELECT
+    date_part('hour', tpep_pickup_datetime) AS trip_hour,
+    percentile_cont(.5)
+        WITHIN GROUP (ORDER BY
+            tpep_dropoff_datetime - tpep_pickup_datetime) AS median_trip
+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
+
+SET timezone TO 'US/Central';
+
+CREATE TABLE train_rides (
+    trip_id bigserial PRIMARY KEY,
+    segment varchar(50) NOT NULL,
+    departure timestamp with time zone NOT NULL,
+    arrival timestamp with time zone NOT NULL
+);
+
+INSERT INTO train_rides (segment, departure, arrival)
+VALUES
+    ('Chicago to New York', '2017-11-13 21:30 CST', '2017-11-14 18:23 EST'),
+    ('New York to New Orleans', '2017-11-15 14:15 EST', '2017-11-16 19:32 CST'),
+    ('New Orleans to Los Angeles', '2017-11-17 13:45 CST', '2017-11-18 9:00 PST'),
+    ('Los Angeles to San Francisco', '2017-11-19 10:10 PST', '2017-11-19 21:24 PST'),
+    ('San Francisco to Denver', '2017-11-20 9:10 PST', '2017-11-21 18:38 MST'),
+    ('Denver to Chicago', '2017-11-22 19:10 MST', '2017-11-23 14:50 CST');
+
+SELECT * FROM train_rides;
+
+-- Listing 11-12: 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
+
+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
+
+SELECT segment,
+       arrival - departure AS segment_time,
+       sum(date_part('epoch', (arrival - departure)))
+           OVER (ORDER BY trip_id) * interval '1 second' AS cume_time
+FROM train_rides;
--- a/Chapter_11/README.md
+++ b/Chapter_11/README.md
@ -0,0 +1,9 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 11: Working with Dates and Times
+
+Explains how to create, manipulate, and query dates and times in your database, including working with time zones, using data on New York City taxi trips and Amtrak train schedules.
+
+
--- a/Chapter_11/yellow_tripdata_2016_06_01.csv
+++ b/Chapter_11/yellow_tripdata_2016_06_01.csv
--- a/Chapter_12/Chapter_12.sql
+++ b/Chapter_12/Chapter_12.sql
@ -0,0 +1,310 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 12 Code Examples
+--------------------------------------------------------------
+
+-- Listing 12-1: Using a subquery in a WHERE clause
+
+SELECT geo_name,
+       state_us_abbreviation,
+       p0010001
+FROM us_counties_2010
+WHERE p0010001 >= (
+    SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY p0010001)
+    FROM us_counties_2010
+    )
+ORDER BY p0010001 DESC;
+
+-- Listing 12-2: Using a subquery in a WHERE clause for DELETE
+
+CREATE TABLE us_counties_2010_top10 AS
+SELECT * FROM us_counties_2010;
+
+DELETE FROM us_counties_2010_top10
+WHERE p0010001 < (
+    SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY p0010001)
+    FROM us_counties_2010_top10
+    );
+
+SELECT count(*) FROM us_counties_2010_top10;
+
+-- Listing 12-3: Subquery as a derived table in a FROM clause
+
+SELECT round(calcs.average, 0) as average,
+       calcs.median,
+       round(calcs.average - calcs.median, 0) AS median_average_diff
+FROM (
+     SELECT avg(p0010001) AS average,
+            percentile_cont(.5)
+                WITHIN GROUP (ORDER BY p0010001)::numeric(10,1) AS median
+     FROM us_counties_2010
+     )
+AS calcs;
+
+-- Listing 12-4: Joining two derived tables
+
+SELECT census.state_us_abbreviation AS st,
+       census.st_population,
+       plants.plant_count,
+       round((plants.plant_count/census.st_population::numeric(10,1)) * 1000000, 1)
+           AS plants_per_million
+FROM
+    (
+         SELECT st,
+                count(*) AS plant_count
+         FROM meat_poultry_egg_inspect
+         GROUP BY st
+    )
+    AS plants
+JOIN
+    (
+        SELECT state_us_abbreviation,
+               sum(p0010001) AS st_population
+        FROM us_counties_2010
+        GROUP BY state_us_abbreviation
+    )
+    AS census
+ON plants.st = census.state_us_abbreviation
+ORDER BY plants_per_million DESC;
+
+-- Listing 12-5: Adding a subquery to a column list
+
+SELECT geo_name,
+       state_us_abbreviation AS st,
+       p0010001 AS total_pop,
+       (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
+        FROM us_counties_2010) AS us_median
+FROM us_counties_2010;
+
+-- Listing 12-6: Using a subquery expression in a calculation
+
+SELECT geo_name,
+       state_us_abbreviation AS st,
+       p0010001 AS total_pop,
+       (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
+        FROM us_counties_2010) AS us_median,
+       p0010001 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
+                   FROM us_counties_2010) AS diff_from_median
+FROM us_counties_2010
+WHERE (p0010001 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
+                   FROM us_counties_2010))
+       BETWEEN -1000 AND 1000;
+
+            
+-- BONUS: Subquery expressions
+-- If you'd like to try the IN, EXISTS, and NOT EXISTS expressions on pages 199-200,
+-- here's the code to create a retirees table. The queries below are similar
+-- to the hypothetical examples on pages 199 and 200. You will need the
+-- employees table you created in Chapter 6.
+              
+-- Create table and insert data
+CREATE TABLE retirees (
+    id int,
+    first_name varchar(50),
+    last_name varchar(50)
+);
+
+INSERT INTO retirees 
+VALUES (2, 'Lee', 'Smith'),
+       (4, 'Janet', 'King');
+
+-- Generating values for the IN operator
+SELECT first_name, last_name
+FROM employees
+WHERE emp_id IN (
+    SELECT id
+    FROM retirees);
+
+-- Checking whether values exist (returns all rows from employees
+-- if the expression evaluates as true)
+SELECT first_name, last_name
+FROM employees
+WHERE EXISTS (
+    SELECT id
+    FROM retirees);
+
+-- Using a correlated subquery to find matching values from employees
+-- in retirees.
+SELECT first_name, last_name
+FROM employees
+WHERE EXISTS (
+    SELECT id
+    FROM retirees
+    WHERE id = employees.emp_id);
+
+                   
+                   
+-- Listing 12-7: Using a simple CTE to find large counties
+
+WITH
+    large_counties (geo_name, st, p0010001)
+AS
+    (
+        SELECT geo_name, state_us_abbreviation, p0010001
+        FROM us_counties_2010
+        WHERE p0010001 >= 100000
+    )
+SELECT st, count(*)
+FROM large_counties
+GROUP BY st
+ORDER BY count(*) DESC;
+
+-- Bonus: You can also write this query as:
+SELECT state_us_abbreviation, count(*)
+FROM us_counties_2010
+WHERE p0010001 >= 100000
+GROUP BY state_us_abbreviation
+ORDER BY count(*) DESC;
+
+-- Listing 12-8: Using CTEs in a table join
+
+WITH
+    counties (st, population) AS
+    (SELECT state_us_abbreviation, sum(population_count_100_percent)
+     FROM us_counties_2010
+     GROUP BY state_us_abbreviation),
+
+    plants (st, plants) AS
+    (SELECT st, count(*) AS plants
+     FROM meat_poultry_egg_inspect
+     GROUP BY st)
+
+SELECT counties.st,
+       population,
+       plants,
+       round((plants/population::numeric(10,1))*1000000, 1) AS per_million
+FROM counties JOIN plants
+ON counties.st = plants.st
+ORDER BY per_million DESC;
+
+-- Listing 12-9: Using CTEs to minimize redundant code
+
+WITH us_median AS 
+    (SELECT percentile_cont(.5) 
+     WITHIN GROUP (ORDER BY p0010001) AS us_median_pop
+     FROM us_counties_2010)
+
+SELECT geo_name,
+       state_us_abbreviation AS st,
+       p0010001 AS total_pop,
+       us_median_pop,
+       p0010001 - us_median_pop AS diff_from_median 
+FROM us_counties_2010 CROSS JOIN us_median
+WHERE (p0010001 - us_median_pop)
+       BETWEEN -1000 AND 1000;
+
+
+-- Cross tabulations
+-- Install the crosstab() function via the tablefunc module
+
+CREATE EXTENSION tablefunc;
+
+-- Listing 12-10: Creating and filling the ice_cream_survey table
+
+CREATE TABLE ice_cream_survey (
+    response_id integer PRIMARY KEY,
+    office varchar(20),
+    flavor varchar(20)
+);
+
+COPY ice_cream_survey
+FROM 'C:\YourDirectory\ice_cream_survey.csv'
+WITH (FORMAT CSV, HEADER);
+
+-- Listing 12-11: Generating the ice cream survey crosstab
+
+SELECT *
+FROM crosstab('SELECT office,
+                      flavor,
+                      count(*)
+               FROM ice_cream_survey
+               GROUP BY office, flavor
+               ORDER BY office',
+
+              'SELECT flavor
+               FROM ice_cream_survey
+               GROUP BY flavor
+               ORDER BY flavor')
+
+AS (office varchar(20),
+    chocolate bigint,
+    strawberry bigint,
+    vanilla bigint);
+
+-- Listing 12-12: Creating and filling a temperature_readings table
+
+CREATE TABLE temperature_readings (
+    reading_id bigserial PRIMARY KEY,
+    station_name varchar(50),
+    observation_date date,
+    max_temp integer,
+    min_temp integer
+);
+
+COPY temperature_readings 
+     (station_name, observation_date, max_temp, min_temp)
+FROM 'C:\YourDirectory\temperature_readings.csv'
+WITH (FORMAT CSV, HEADER);
+
+-- Listing 12-13: Generating the temperature readings crosstab
+
+SELECT *
+FROM crosstab('SELECT
+                  station_name,
+                  date_part(''month'', observation_date),
+                  percentile_cont(.5)
+                      WITHIN GROUP (ORDER BY max_temp)
+               FROM temperature_readings
+               GROUP BY station_name,
+                        date_part(''month'', observation_date)
+               ORDER BY station_name',
+
+              'SELECT month
+               FROM generate_series(1,12) month')
+
+AS (station varchar(50),
+    jan numeric(3,0),
+    feb numeric(3,0),
+    mar numeric(3,0),
+    apr numeric(3,0),
+    may numeric(3,0),
+    jun numeric(3,0),
+    jul numeric(3,0),
+    aug numeric(3,0),
+    sep numeric(3,0),
+    oct numeric(3,0),
+    nov numeric(3,0),
+    dec numeric(3,0)
+);
+
+-- Listing 12-14: Re-classifying temperature data with CASE
+
+SELECT max_temp,
+       CASE WHEN max_temp >= 90 THEN 'Hot'
+            WHEN max_temp BETWEEN 70 AND 89 THEN 'Warm'
+            WHEN max_temp BETWEEN 50 AND 69 THEN 'Pleasant'
+            WHEN max_temp BETWEEN 33 AND 49 THEN 'Cold'
+            WHEN max_temp BETWEEN 20 AND 32 THEN 'Freezing'
+            ELSE 'Inhumane'
+        END AS temperature_group
+FROM temperature_readings;
+
+-- Listing 12-15: Using CASE in a Common Table Expression
+
+WITH temps_collapsed (station_name, max_temperature_group) AS
+    (SELECT station_name,
+           CASE WHEN max_temp >= 90 THEN 'Hot'
+                WHEN max_temp BETWEEN 70 AND 89 THEN 'Warm'
+                WHEN max_temp BETWEEN 50 AND 69 THEN 'Pleasant'
+                WHEN max_temp BETWEEN 33 AND 49 THEN 'Cold'
+                WHEN max_temp BETWEEN 20 AND 32 THEN 'Freezing'
+                ELSE 'Inhumane'
+            END
+    FROM temperature_readings)
+
+SELECT station_name, max_temperature_group, count(*)
+FROM temps_collapsed
+GROUP BY station_name, max_temperature_group
+ORDER BY station_name, count(*) DESC;
--- a/Chapter_12/README.md
+++ b/Chapter_12/README.md
@ -0,0 +1,11 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 12: Advanced Query Techniques
+
+Explains how to use more complex SQL operations, such as subqueries and cross tabulations, and the CASE statement to reclassify values in a data set on temperature readings.
+
+
+
+
--- a/Chapter_12/ice_cream_survey.csv
+++ b/Chapter_12/ice_cream_survey.csv
@ -0,0 +1 @@
+response_id,office,flavor
1,Uptown,Chocolate
2,Midtown,Chocolate
3,Downtown,Strawberry
4,Uptown,Chocolate
5,Midtown,Chocolate
6,Downtown,Strawberry
7,Uptown,Chocolate
8,Midtown,Chocolate
9,Downtown,Strawberry
10,Downtown,Chocolate
11,Midtown,Chocolate
12,Downtown,Chocolate
13,Uptown,Vanilla
14,Uptown,Strawberry
15,Midtown,Chocolate
16,Downtown,Strawberry
17,Midtown,Chocolate
18,Downtown,Chocolate
19,Uptown,Vanilla
20,Midtown,Chocolate
21,Uptown,Strawberry
22,Uptown,Chocolate
23,Midtown,Chocolate
24,Downtown,Strawberry
25,Midtown,Chocolate
26,Midtown,Chocolate
27,Downtown,Strawberry
28,Uptown,Strawberry
29,Uptown,Chocolate
30,Uptown,Vanilla
31,Midtown,Chocolate
32,Downtown,Chocolate
33,Uptown,Vanilla
34,Uptown,Chocolate
35,Uptown,Vanilla
36,Midtown,Chocolate
37,Downtown,Strawberry
38,Uptown,Strawberry
39,Midtown,Chocolate
40,Downtown,Chocolate
41,Downtown,Chocolate
42,Midtown,Chocolate
43,Downtown,Strawberry
44,Uptown,Strawberry
45,Uptown,Chocolate
46,Midtown,Chocolate
47,Downtown,Strawberry
48,Uptown,Vanilla
49,Midtown,Chocolate
50,Downtown,Chocolate
51,Midtown,Chocolate
52,Downtown,Strawberry
53,Uptown,Chocolate
54,Uptown,Vanilla
55,Uptown,Strawberry
56,Midtown,Chocolate
57,Midtown,Chocolate
58,Downtown,Strawberry
59,Uptown,Chocolate
60,Midtown,Chocolate
61,Downtown,Strawberry
62,Midtown,Chocolate
63,Downtown,Strawberry
64,Uptown,Vanilla
65,Midtown,Chocolate
66,Downtown,Chocolate
67,Uptown,Chocolate
68,Midtown,Chocolate
69,Downtown,Strawberry
70,Downtown,Chocolate
71,Midtown,Chocolate
72,Uptown,Strawberry
73,Midtown,Chocolate
74,Downtown,Strawberry
75,Midtown,Chocolate
76,Downtown,Strawberry
77,Uptown,Strawberry
78,Uptown,Chocolate
79,Downtown,Strawberry
80,Uptown,Chocolate
81,Midtown,Vanilla
82,Downtown,Strawberry
83,Midtown,Vanilla
84,Downtown,Strawberry
85,Uptown,Vanilla
86,Uptown,Vanilla
87,Midtown,Chocolate
88,Downtown,Vanilla
89,Midtown,Chocolate
90,Downtown,Vanilla
91,Uptown,Chocolate
92,Uptown,Chocolate
93,Uptown,Vanilla
94,Midtown,Chocolate
95,Downtown,Vanilla
96,Uptown,Strawberry
97,Downtown,Vanilla
98,Uptown,Chocolate
99,Midtown,Vanilla
100,Downtown,Chocolate
101,Uptown,Vanilla
102,Uptown,Strawberry
103,Midtown,Vanilla
104,Downtown,Strawberry
105,Uptown,Chocolate
106,Uptown,Vanilla
107,Midtown,Chocolate
108,Downtown,Chocolate
109,Midtown,Vanilla
110,Downtown,Chocolate
111,Uptown,Vanilla
112,Uptown,Strawberry
113,Uptown,Vanilla
114,Midtown,Chocolate
115,Downtown,Vanilla
116,Uptown,Vanilla
117,Uptown,Strawberry
118,Midtown,Vanilla
119,Uptown,Strawberry
120,Midtown,Vanilla
121,Uptown,Vanilla
122,Midtown,Vanilla
123,Downtown,Chocolate
124,Uptown,Vanilla
125,Midtown,Chocolate
126,Downtown,Vanilla
127,Uptown,Chocolate
128,Midtown,Vanilla
129,Downtown,Chocolate
130,Uptown,Vanilla
131,Uptown,Strawberry
132,Midtown,Vanilla
133,Downtown,Chocolate
134,Downtown,Chocolate
135,Midtown,Vanilla
136,Downtown,Chocolate
137,Uptown,Vanilla
138,Midtown,Vanilla
139,Uptown,Vanilla
140,Midtown,Vanilla
141,Downtown,Strawberry
142,Downtown,Strawberry
143,Midtown,Chocolate
144,Downtown,Vanilla
145,Downtown,Strawberry
146,Midtown,Chocolate
147,Downtown,Vanilla
148,Uptown,Strawberry
149,Midtown,Chocolate
150,Downtown,Vanilla
151,Downtown,Strawberry
152,Midtown,Chocolate
153,Downtown,Chocolate
154,Midtown,Chocolate
155,Downtown,Vanilla
156,Uptown,Strawberry
157,Uptown,Chocolate
158,Midtown,Vanilla
159,Downtown,Strawberry
160,Downtown,Chocolate
161,Midtown,Vanilla
162,Downtown,Strawberry
163,Uptown,Chocolate
164,Midtown,Chocolate
165,Downtown,Vanilla
166,Downtown,Vanilla
167,Midtown,Vanilla
168,Uptown,Strawberry
169,Midtown,Chocolate
170,Downtown,Vanilla
171,Downtown,Strawberry
172,Midtown,Chocolate
173,Downtown,Vanilla
174,Midtown,Vanilla
175,Downtown,Vanilla
176,Downtown,Chocolate
177,Uptown,Vanilla
178,Midtown,Vanilla
179,Downtown,Chocolate
180,Downtown,Vanilla
181,Midtown,Vanilla
182,Downtown,Chocolate
183,Uptown,Chocolate
184,Midtown,Vanilla
185,Downtown,Chocolate
186,Downtown,Vanilla
187,Midtown,Vanilla
188,Downtown,Strawberry
189,Midtown,Vanilla
190,Downtown,Strawberry
191,Downtown,Strawberry
192,Uptown,Chocolate
193,Uptown,Vanilla
194,Midtown,Vanilla
195,Downtown,Strawberry
196,Downtown,Vanilla
197,Downtown,Strawberry
198,Uptown,Chocolate
199,Midtown,Chocolate
200,Downtown,Vanilla
--- a/Chapter_12/temperature_readings.csv
+++ b/Chapter_12/temperature_readings.csv
--- a/Chapter_13/Chapter_13.sql
+++ b/Chapter_13/Chapter_13.sql
@ -0,0 +1,363 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 13 Code Examples
+--------------------------------------------------------------
+
+-- Commonly used string functions
+-- Full list at https://www.postgresql.org/docs/current/static/functions-string.html
+
+-- Case formatting
+SELECT upper('Neal7');
+SELECT lower('Randy');
+SELECT initcap('at the end of the day');
+-- Note initcap's imperfect for acronyms
+SELECT initcap('Practical SQL');
+
+-- Character Information
+SELECT char_length(' Pat ');
+SELECT length(' Pat ');
+SELECT position(', ' in 'Tan, Bella');
+
+-- Removing characters
+SELECT trim('s' from 'socks');
+SELECT trim(trailing 's' from 'socks');
+SELECT trim(' Pat ');
+SELECT char_length(trim(' Pat ')); -- note the length change
+SELECT ltrim('socks', 's');
+SELECT rtrim('socks', 's');
+
+-- Extracting and replacing characters
+SELECT left('703-555-1212', 3);
+SELECT right('703-555-1212', 8);
+SELECT replace('bat', 'b', 'c');
+
+
+-- Table 13-2: Regular Expression Matching Examples
+
+-- Any character one or more times
+SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from '.+');
+-- One or two digits followed by a space and p.m.
+SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from '\d{1,2} (?:a.m.|p.m.)');
+-- One or more word characters at the start
+SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from '^\w+');
+-- One or more word characters followed by any character at the end.
+SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from '\w+.$');
+-- The words May or June
+SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from 'May|June');
+-- Four digits
+SELECT substring('The game starts at 7 p.m. on May 2, 2019.' from '\d{4}');
+-- May followed by a space, digit, comma, space, and four digits.
+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
+-- Data from https://sheriff.loudoun.gov/dailycrime
+
+CREATE TABLE crime_reports (
+    crime_id bigserial PRIMARY KEY,
+    date_1 timestamp with time zone,
+    date_2 timestamp with time zone,
+    street varchar(250),
+    city varchar(100),
+    crime_type varchar(100),
+    description text,
+    case_number varchar(50),
+    original_text text NOT NULL
+);
+
+COPY crime_reports (original_text)
+FROM 'C:\YourDirectory\crime_reports.csv'
+WITH (FORMAT CSV, HEADER OFF, QUOTE '"');
+
+SELECT original_text FROM crime_reports;
+
+-- Listing 13-3: 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
+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
+-- 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
+-- 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
+
+SELECT
+    regexp_match(original_text, '(?:C0|SO)[0-9]+') AS case_number,
+    regexp_match(original_text, '\d{1,2}\/\d{1,2}\/\d{2}') AS date_1,
+    regexp_match(original_text, '\n(?:\w+ \w+|\w+)\n(.*):') AS crime_type,
+    regexp_match(original_text, '(?:Sq.|Plz.|Dr.|Ter.|Rd.)\n(\w+ \w+|\w+)\n')
+        AS city
+FROM crime_reports;
+
+-- Bonus: Get all parsed elements at once
+
+SELECT crime_id,
+       regexp_match(original_text, '\d{1,2}\/\d{1,2}\/\d{2}') AS date_1,
+       CASE WHEN EXISTS (SELECT regexp_matches(original_text, '-(\d{1,2}\/\d{1,2}\/\d{1,2})'))
+            THEN regexp_match(original_text, '-(\d{1,2}\/\d{1,2}\/\d{1,2})')
+            ELSE NULL
+            END AS date_2,
+       regexp_match(original_text, '\/\d{2}\n(\d{4})') AS hour_1,
+       CASE WHEN EXISTS (SELECT regexp_matches(original_text, '\/\d{2}\n\d{4}-(\d{4})'))
+            THEN regexp_match(original_text, '\/\d{2}\n\d{4}-(\d{4})')
+            ELSE NULL
+            END AS hour_2,
+       regexp_match(original_text, 'hrs.\n(\d+ .+(?:Sq.|Plz.|Dr.|Ter.|Rd.))') AS street,
+       regexp_match(original_text, '(?:Sq.|Plz.|Dr.|Ter.|Rd.)\n(\w+ \w+|\w+)\n') AS city,
+       regexp_match(original_text, '\n(?:\w+ \w+|\w+)\n(.*):') AS crime_type,
+       regexp_match(original_text, ':\s(.+)(?:C0|SO)') AS description,
+       regexp_match(original_text, '(?:C0|SO)[0-9]+') AS case_number
+FROM crime_reports;
+
+-- Listing 13-8: Retrieving a value from within an array
+
+SELECT
+    crime_id,
+    (regexp_match(original_text, '(?:C0|SO)[0-9]+'))[1]
+        AS case_number
+FROM crime_reports;
+
+-- Listing 13-9: Updating the crime_reports date_1 column
+
+UPDATE crime_reports
+SET date_1 = 
+(
+    (regexp_match(original_text, '\d{1,2}\/\d{1,2}\/\d{2}'))[1]
+        || ' ' ||
+    (regexp_match(original_text, '\/\d{2}\n(\d{4})'))[1] 
+        ||' US/Eastern'
+)::timestamptz;
+
+SELECT crime_id,
+       date_1,
+       original_text
+FROM crime_reports;
+
+-- Listing 13-10: Updating all crime_reports columns
+
+UPDATE crime_reports
+SET date_1 = 
+    (
+      (regexp_match(original_text, '\d{1,2}\/\d{1,2}\/\d{2}'))[1]
+          || ' ' ||
+      (regexp_match(original_text, '\/\d{2}\n(\d{4})'))[1] 
+          ||' US/Eastern'
+    )::timestamptz,
+             
+    date_2 = 
+    CASE 
+    -- if there is no second date but there is a second hour
+        WHEN (SELECT regexp_match(original_text, '-(\d{1,2}\/\d{1,2}\/\d{1,2})') IS NULL)
+                     AND (SELECT regexp_match(original_text, '\/\d{2}\n\d{4}-(\d{4})') IS NOT NULL)
+        THEN 
+          ((regexp_match(original_text, '\d{1,2}\/\d{1,2}\/\d{2}'))[1]
+              || ' ' ||
+          (regexp_match(original_text, '\/\d{2}\n\d{4}-(\d{4})'))[1] 
+              ||' US/Eastern'
+          )::timestamptz 
+
+    -- if there is both a second date and second hour
+        WHEN (SELECT regexp_match(original_text, '-(\d{1,2}\/\d{1,2}\/\d{1,2})') IS NOT NULL)
+              AND (SELECT regexp_match(original_text, '\/\d{2}\n\d{4}-(\d{4})') IS NOT NULL)
+        THEN 
+          ((regexp_match(original_text, '-(\d{1,2}\/\d{1,2}\/\d{1,2})'))[1]
+              || ' ' ||
+          (regexp_match(original_text, '\/\d{2}\n\d{4}-(\d{4})'))[1] 
+              ||' US/Eastern'
+          )::timestamptz 
+    -- if neither of those conditions exist, provide a NULL
+        ELSE NULL 
+    END,
+    street = (regexp_match(original_text, 'hrs.\n(\d+ .+(?:Sq.|Plz.|Dr.|Ter.|Rd.))'))[1],
+    city = (regexp_match(original_text,
+                           '(?:Sq.|Plz.|Dr.|Ter.|Rd.)\n(\w+ \w+|\w+)\n'))[1],
+    crime_type = (regexp_match(original_text, '\n(?:\w+ \w+|\w+)\n(.*):'))[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
+
+SELECT date_1,
+       street,
+       city,
+       crime_type
+FROM crime_reports;
+
+-- Listing 13-12: Using regular expressions in a WHERE clause
+
+SELECT geo_name
+FROM us_counties_2010
+WHERE geo_name ~* '(.+lade.+|.+lare.+)'
+ORDER BY geo_name;
+
+SELECT geo_name
+FROM us_counties_2010
+WHERE geo_name ~* '.+ash.+' AND geo_name !~ 'Wash.+'
+ORDER BY geo_name;
+
+
+-- Listing 13-13: Regular expression functions to replace and split
+
+SELECT regexp_replace('05/12/2018', '\d{4}', '2017');
+
+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
+
+SELECT array_length(regexp_split_to_array('Phil Mike Tony Steve', ' '), 1);
+
+
+-- FULL TEXT SEARCH
+
+-- Full-text search operators:
+-- & (AND)
+-- | (OR)
+-- ! (NOT)
+
+-- Listing 13-15: 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
+
+SELECT to_tsquery('walking & sitting');
+
+-- Listing 13-17: 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
+
+-- Sources:
+-- https://archive.org/details/State-of-the-Union-Addresses-1945-2006
+-- http://www.presidency.ucsb.edu/ws/index.php
+-- https://www.eisenhower.archives.gov/all_about_ike/speeches.html
+
+CREATE TABLE president_speeches (
+    sotu_id serial PRIMARY KEY,
+    president varchar(100) NOT NULL,
+    title varchar(250) NOT NULL,
+    speech_date date NOT NULL,
+    speech_text text NOT NULL,
+    search_speech_text tsvector
+);
+
+COPY president_speeches (president, title, speech_date, speech_text)
+FROM 'C:\YourDirectory\sotu-1946-1977.csv'
+WITH (FORMAT CSV, DELIMITER '|', HEADER OFF, QUOTE '@');
+
+SELECT * FROM president_speeches;
+
+-- Listing 13-19: 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
+
+CREATE INDEX search_idx ON president_speeches USING gin(search_speech_text);
+
+-- Listing 13-21: 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()
+
+SELECT president,
+       speech_date,
+       ts_headline(speech_text, to_tsquery('Vietnam'),
+                   'StartSel = <,
+                    StopSel = >,
+                    MinWords=5,
+                    MaxWords=7,
+                    MaxFragments=1')
+FROM president_speeches
+WHERE search_speech_text @@ to_tsquery('Vietnam');
+
+-- Listing 13-23: Finding speeches with the word "transportation" but not "roads"
+
+SELECT president,
+       speech_date,
+       ts_headline(speech_text, to_tsquery('transportation & !roads'),
+                   'StartSel = <,
+                    StopSel = >,
+                    MinWords=5,
+                    MaxWords=7,
+                    MaxFragments=1')
+FROM president_speeches
+WHERE search_speech_text @@ to_tsquery('transportation & !roads');
+
+-- Listing 13-24: Find speeches where "defense" follows "military"
+
+SELECT president,
+       speech_date,
+       ts_headline(speech_text, to_tsquery('military <-> defense'),
+                   'StartSel = <,
+                    StopSel = >,
+                    MinWords=5,
+                    MaxWords=7,
+                    MaxFragments=1')
+FROM president_speeches
+WHERE search_speech_text @@ to_tsquery('military <-> defense');
+
+-- Bonus: Example with a distance of 2:
+SELECT president,
+       speech_date,
+       ts_headline(speech_text, to_tsquery('military <2> defense'),
+                   'StartSel = <,
+                    StopSel = >,
+                    MinWords=5,
+                    MaxWords=7,
+                    MaxFragments=2')
+FROM president_speeches
+WHERE search_speech_text @@ to_tsquery('military <2> defense');
+
+-- Listing 13-25: Scoring relevance with ts_rank()
+
+SELECT president,
+       speech_date,
+       ts_rank(search_speech_text,
+               to_tsquery('war & security & threat & enemy')) AS score
+FROM president_speeches
+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
+
+SELECT president,
+       speech_date,
+       ts_rank(search_speech_text,
+               to_tsquery('war & security & threat & enemy'), 2)::numeric 
+               AS score
+FROM president_speeches
+WHERE search_speech_text @@ to_tsquery('war & security & threat & enemy')
+ORDER BY score DESC
+LIMIT 5;
+
+
--- a/Chapter_13/README.md
+++ b/Chapter_13/README.md
@ -0,0 +1,10 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 13: Mining Text to Find Meaningful Data
+
+Covers how to use PostgreSQL’s full text search engine and regular expressions to extract data from unstructured text, using a collection of speeches by U.S. presidents as an example.
+
+
+
--- a/Chapter_13/crime_reports.csv
+++ b/Chapter_13/crime_reports.csv
@ -0,0 +1,39 @@
+"4/16/17-4/17/17
+2100-0900 hrs.
+46000 Block Ashmere Sq.
+Sterling
+Larceny: The victim reported that a
+bicycle was stolen from their opened
+garage door during the overnight hours.
+C0170006614"
+"4/8/17
+1600 hrs.
+46000 Block Potomac Run Plz.
+Sterling
+Destruction of Property: The victim
+reported that their vehicle was spray
+painted and the trim was ripped off while
+it was parked at this location.
+C0170006162"
+"4/4/17
+1400-1500 hrs.
+24000 Block Hawthorn Thicket Ter.
+Sterling
+Larceny: The complainant reported that
+multiple windows were stolen from this home
+under construction. C0170006079"
+"04/10/17
+1605 hrs.
+21800 block Newlin Mill Rd.
+Middleburg
+Larceny: A license plate was reported
+stolen from a vehicle.
+SO170006250"
+"04/09/17
+1200 hrs.
+470000 block Fairway Dr.
+Sterling
+Destruction of Property: Unknown
+subject(s) wrote graffiti on a sign in the
+area.
+SO170006211"
--- a/Chapter_13/sotu-1946-1977.csv
+++ b/Chapter_13/sotu-1946-1977.csv
--- a/Chapter_14/Chapter_14.sql
+++ b/Chapter_14/Chapter_14.sql
@ -0,0 +1,252 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 14 Code Examples
+--------------------------------------------------------------
+
+-- Listing 14-1: 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
+
+CREATE EXTENSION postgis;
+
+SELECT postgis_full_version(); -- shows PostGIS version
+
+-- Listing 14-3: 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
+
+SELECT ST_GeomFromText('POINT(-74.9233606 42.699992)', 4326);
+
+SELECT ST_GeomFromText('LINESTRING(-74.9 42.7, -75.1 42.7)', 4326);
+
+SELECT ST_GeomFromText('POLYGON((-74.9 42.7, -75.1 42.7,
+                                 -75.1 42.6, -74.9 42.7))', 4326);
+
+SELECT ST_GeomFromText('MULTIPOINT (-74.9 42.7, -75.1 42.7)', 4326);
+
+SELECT ST_GeomFromText('MULTILINESTRING((-76.27 43.1, -76.06 43.08),
+                                        (-76.2 43.3, -76.2 43.4,
+                                         -76.4 43.1))', 4326);
+
+SELECT ST_GeomFromText('MULTIPOLYGON((
+                                     (-74.92 42.7, -75.06 42.71,
+                                      -75.07 42.64, -74.92 42.7),
+                                     (-75.0 42.66, -75.0 42.64,
+                                      -74.98 42.64, -74.98 42.66,
+                                      -75.0 42.66)))', 4326);
+
+-- Listing 14-5: 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
+
+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
+
+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
+
+SELECT ST_PolygonFromText('POLYGON((-74.9 42.7, -75.1 42.7,
+                                    -75.1 42.6, -74.9 42.7))', 4326);
+
+SELECT ST_MakePolygon(
+           ST_GeomFromText('LINESTRING(-74.92 42.7, -75.06 42.71,
+                                       -75.07 42.64, -74.92 42.7)', 4326));
+
+SELECT ST_MPolyFromText('MULTIPOLYGON((
+                                       (-74.92 42.7, -75.06 42.71,
+                                        -75.07 42.64, -74.92 42.7),
+                                       (-75.0 42.66, -75.0 42.64,
+                                        -74.98 42.64, -74.98 42.66,
+                                        -75.0 42.66)
+                                      ))', 4326);
+
+
+-- ANALYZING FARMERS MARKETS DATA
+-- https://catalog.data.gov/dataset/farmers-markets-geographic-data
+-- https://www.ams.usda.gov/local-food-directories/farmersmarkets
+
+
+-- Listing 14-9: Create and load the farmers_markets table
+
+CREATE TABLE farmers_markets (
+    fmid bigint PRIMARY KEY,
+    market_name varchar(100) NOT NULL,
+    street varchar(180),
+    city varchar(60),
+    county varchar(25),
+    st varchar(20) NOT NULL,
+    zip varchar(10),
+    longitude numeric(10,7),
+    latitude numeric(10,7),
+    organic varchar(1) NOT NULL
+);
+
+COPY farmers_markets
+FROM 'C:\YourDirectory\farmers_markets.csv'
+WITH (FORMAT CSV, HEADER);
+
+SELECT count(*) FROM farmers_markets; -- should return 8,681 rows
+
+-- Listing 14-10: Creating and indexing a geography column
+-- There's also a function: https://postgis.net/docs/AddGeometryColumn.html
+
+-- Add column
+ALTER TABLE farmers_markets ADD COLUMN geog_point geography(POINT,4326);
+
+-- Now fill that column with the lat/long
+UPDATE farmers_markets
+SET geog_point = ST_SetSRID(
+                            ST_MakePoint(longitude,latitude),4326
+                           )::geography;
+
+-- Add a GiST index
+CREATE INDEX market_pts_idx ON farmers_markets USING GIST (geog_point);
+
+-- View the geography column
+SELECT longitude,
+       latitude,
+       geog_point,
+       ST_AsText(geog_point)
+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
+
+SELECT market_name,
+       city,
+       st
+FROM farmers_markets
+WHERE ST_DWithin(geog_point,
+                 ST_GeogFromText('POINT(-93.6204386 41.5853202)'),
+                 10000)
+ORDER BY market_name;
+
+-- Listing 14-12: Using ST_Distance() to calculate the miles between Yankee Stadium
+-- and Citi Field (Mets)
+-- 1609.344 meters/mile
+
+SELECT ST_Distance(
+                   ST_GeogFromText('POINT(-73.9283685 40.8296466)'),
+                   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
+
+SELECT market_name,
+       city,
+       round(
+           (ST_Distance(geog_point,
+                        ST_GeogFromText('POINT(-93.6204386 41.5853202)')
+                        ) / 1609.344)::numeric(8,5), 2
+            ) AS miles_from_dt
+FROM farmers_markets
+WHERE ST_DWithin(geog_point,
+                 ST_GeogFromText('POINT(-93.6204386 41.5853202)'),
+                 10000)
+ORDER BY miles_from_dt ASC;
+
+
+-- WORKING WITH SHAPEFILES
+
+-- Resources:
+   -- TIGER/Line® Shapefiles and TIGER/Line® Files
+   -- https://www.census.gov/geo/maps-data/data/tiger-line.html
+   -- 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
+
+SELECT ST_AsText(geom)
+FROM us_counties_2010_shp
+LIMIT 1;
+
+-- Listing 14-15: Find the largest counties by area using ST_Area()
+
+SELECT name10,
+       statefp10 AS st,
+       round(
+             ( ST_Area(geom::geography) / 2589988.110336 )::numeric, 2
+            )  AS square_miles
+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
+
+SELECT name10,
+       statefp10
+FROM us_counties_2010_shp
+WHERE ST_Within('SRID=4269;POINT(-118.3419063 34.0977076)'::geometry, geom);
+
+-- SPATIAL JOINS
+-- SANTA FE WATER AND ROAD DATA
+-- http://www.santafenm.gov/santa_fe_river
+
+-- Census 2016 Tiger/Line roads, water
+-- https://www.census.gov/geo/maps-data/data/tiger-line.html
+-- https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2016&layergroup=Roads
+-- https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2016&layergroup=Water
+
+-- RTTYP - Route Type Code Description
+-- https://www.census.gov/geo/reference/rttyp.html
+-- C County
+-- I Interstate
+-- M Common Name
+-- O Other
+-- S State recognized
+-- U U.S.
+
+-- MTFCC MAF/TIGER feature class code
+-- https://www.census.gov/geo/reference/mtfcc.html
+-- Here, H3010: A natural flowing waterway
+
+-- Listing 14-17: Using ST_GeometryType() to determine geometry
+
+SELECT ST_GeometryType(geom)
+FROM santafe_linearwater_2016
+LIMIT 1;
+
+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
+
+SELECT water.fullname AS waterway,
+       roads.rttyp,
+       roads.fullname AS road
+FROM santafe_linearwater_2016 water JOIN santafe_roads_2016 roads
+    ON ST_Intersects(water.geom, roads.geom)
+WHERE water.fullname = 'Santa Fe Riv'
+ORDER BY roads.fullname;
+
+-- Listing 14-19: Using ST_Intersection() to show where roads cross the river
+
+SELECT water.fullname AS waterway,
+       roads.rttyp,
+       roads.fullname AS road,
+       ST_AsText(ST_Intersection(water.geom, roads.geom))
+FROM santafe_linearwater_2016 water JOIN santafe_roads_2016 roads
+    ON ST_Intersects(water.geom, roads.geom)
+WHERE water.fullname = 'Santa Fe Riv'
+ORDER BY roads.fullname
+LIMIT 5;
--- a/Chapter_14/README.md
+++ b/Chapter_14/README.md
@ -0,0 +1,10 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 14: Analyzing Spatial Data with PostGIS
+
+Introduces data types and queries related to spatial objects, which will let you analyze geographical features like states, roads, and rivers.
+
+
+
--- a/Chapter_14/farmers_markets.csv
+++ b/Chapter_14/farmers_markets.csv
--- a/Chapter_14/tl_2010_us_county10.zip
+++ b/Chapter_14/tl_2010_us_county10.zip
--- a/Chapter_14/tl_2016_35049_linearwater.zip
+++ b/Chapter_14/tl_2016_35049_linearwater.zip
--- a/Chapter_14/tl_2016_35049_roads.zip
+++ b/Chapter_14/tl_2016_35049_roads.zip
--- a/Chapter_15/Chapter_15.sql
+++ b/Chapter_15/Chapter_15.sql
@ -0,0 +1,321 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 15 Code Examples
+--------------------------------------------------------------
+
+-- VIEWS
+
+-- Listing 15-1: Creating a view that displays Nevada 2010 counties
+
+CREATE OR REPLACE VIEW nevada_counties_pop_2010 AS
+    SELECT geo_name,
+           state_fips,
+           county_fips,
+           p0010001 AS pop_2010
+    FROM us_counties_2010
+    WHERE state_us_abbreviation = 'NV'
+    ORDER BY county_fips;
+
+-- Listing 15-2: 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
+
+CREATE OR REPLACE VIEW county_pop_change_2010_2000 AS
+    SELECT c2010.geo_name,
+           c2010.state_us_abbreviation AS st,
+           c2010.state_fips,
+           c2010.county_fips,
+           c2010.p0010001 AS pop_2010,
+           c2000.p0010001 AS pop_2000,
+           round( (CAST(c2010.p0010001 AS numeric(8,1)) - c2000.p0010001)
+               / c2000.p0010001 * 100, 1 ) AS pct_change_2010_2000
+    FROM us_counties_2010 c2010 INNER JOIN us_counties_2000 c2000
+    ON c2010.state_fips = c2000.state_fips
+       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
+
+SELECT geo_name,
+       st,
+       pop_2010,
+       pct_change_2010_2000
+FROM county_pop_change_2010_2000
+WHERE st = 'NV'
+LIMIT 5;
+
+-- Listing 15-5: Creating a view on the employees table
+
+CREATE OR REPLACE VIEW employees_tax_dept AS
+     SELECT emp_id,
+            first_name,
+            last_name,
+            dept_id
+     FROM employees
+     WHERE dept_id = 1
+     ORDER BY emp_id
+     WITH LOCAL CHECK OPTION;
+
+SELECT * FROM employees_tax_dept;
+
+-- Listing 15-6: 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);
+
+INSERT INTO employees_tax_dept (first_name, last_name, dept_id)
+VALUES ('Jamil', 'White', 2);
+
+-- optional:
+SELECT * FROM employees_tax_dept;
+
+SELECT * FROM employees;
+
+-- Listing 15-7: Updating a row via the employees_tax_dept view
+
+UPDATE employees_tax_dept
+SET last_name = 'Le Gere'
+WHERE emp_id = 5;
+
+SELECT * FROM employees_tax_dept;
+
+-- Bonus: This will fail because the salary column is not in the view
+UPDATE employees_tax_dept
+SET salary = 100000
+WHERE emp_id = 5;
+
+-- Listing 15-8: Deleting a row via the employees_tax_dept view
+
+DELETE FROM employees_tax_dept
+WHERE emp_id = 5;
+
+
+-- FUNCTIONS
+-- https://www.postgresql.org/docs/current/static/plpgsql.html
+
+-- Listing 15-9: Creating a percent_change function
+-- To delete this function: DROP FUNCTION percent_change(numeric,numeric,integer);
+
+CREATE OR REPLACE FUNCTION
+percent_change(new_value numeric,
+               old_value numeric,
+               decimal_places integer DEFAULT 1)
+RETURNS numeric AS
+'SELECT round(
+        ((new_value - old_value) / old_value) * 100, decimal_places
+);'
+LANGUAGE SQL
+IMMUTABLE
+RETURNS NULL ON NULL INPUT;
+
+-- Listing 15-10: Testing the percent_change() function
+
+SELECT percent_change(110, 108, 2);
+
+-- Listing 15-11: Testing percent_change() on Census data
+
+SELECT c2010.geo_name,
+       c2010.state_us_abbreviation AS st,
+       c2010.p0010001 AS pop_2010,
+       percent_change(c2010.p0010001, c2000.p0010001) AS pct_chg_func,
+       round( (CAST(c2010.p0010001 AS numeric(8,1)) - c2000.p0010001)
+           / c2000.p0010001 * 100, 1 ) AS pct_chg_formula
+FROM us_counties_2010 c2010 INNER JOIN us_counties_2000 c2000
+ON c2010.state_fips = c2000.state_fips
+   AND c2010.county_fips = c2000.county_fips
+ORDER BY pct_chg_func DESC
+LIMIT 5;
+
+-- Listing 15-12: Adding a column to the teachers table and seeing the data
+
+ALTER TABLE teachers ADD COLUMN personal_days integer;
+
+SELECT first_name,
+       last_name,
+       hire_date,
+       personal_days
+FROM teachers;
+
+-- Listing 15-13: Creating an update_personal_days() function
+
+CREATE OR REPLACE FUNCTION update_personal_days()
+RETURNS void AS $$
+BEGIN
+    UPDATE teachers
+    SET personal_days =
+        CASE WHEN (now() - hire_date) BETWEEN '5 years'::interval
+                                      AND '10 years'::interval THEN 4
+             WHEN (now() - hire_date) > '10 years'::interval THEN 5
+             ELSE 3
+        END;
+    RAISE NOTICE 'personal_days updated!';
+END;
+$$ LANGUAGE plpgsql;
+
+-- To run the function:
+SELECT update_personal_days();
+
+-- Listing 15-14: Enabling the PL/Python procedural language
+
+CREATE EXTENSION plpythonu;
+
+-- Listing 15-15: Using PL/Python to create the trim_county() function
+
+CREATE OR REPLACE FUNCTION trim_county(input_string text)
+RETURNS text AS $$
+    import re
+    cleaned = re.sub(r' County', '', input_string)
+    return cleaned
+$$ LANGUAGE plpythonu;
+
+-- Listing 15-16: Testing the trim_county() function
+
+SELECT geo_name,
+       trim_county(geo_name)
+FROM us_counties_2010
+ORDER BY state_fips, county_fips
+LIMIT 5;
+
+
+-- TRIGGERS
+
+-- Listing 15-17: Creating the grades and grades_history tables
+
+CREATE TABLE grades (
+    student_id bigint,
+    course_id bigint,
+    course varchar(30) NOT NULL,
+    grade varchar(5) NOT NULL,
+PRIMARY KEY (student_id, course_id)
+);
+
+INSERT INTO grades
+VALUES
+    (1, 1, 'Biology 2', 'F'),
+    (1, 2, 'English 11B', 'D'),
+    (1, 3, 'World History 11B', 'C'),
+    (1, 4, 'Trig 2', 'B');
+
+CREATE TABLE grades_history (
+    student_id bigint NOT NULL,
+    course_id bigint NOT NULL,
+    change_time timestamp with time zone NOT NULL,
+    course varchar(30) NOT NULL,
+    old_grade varchar(5) NOT NULL,
+    new_grade varchar(5) NOT NULL,
+PRIMARY KEY (student_id, course_id, change_time)
+);  
+
+-- Listing 15-18: Creating the record_if_grade_changed() function
+
+CREATE OR REPLACE FUNCTION record_if_grade_changed()
+    RETURNS trigger AS
+$$
+BEGIN
+    IF NEW.grade <> OLD.grade THEN
+    INSERT INTO grades_history (
+        student_id,
+        course_id,
+        change_time,
+        course,
+        old_grade,
+        new_grade)
+    VALUES
+        (OLD.student_id,
+         OLD.course_id,
+         now(),
+         OLD.course,
+         OLD.grade,
+         NEW.grade);
+    END IF;
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Listing 15-19: Creating the grades_update trigger
+
+CREATE TRIGGER grades_update
+  AFTER UPDATE
+  ON grades
+  FOR EACH ROW
+  EXECUTE PROCEDURE record_if_grade_changed();
+
+-- Listing 15-20: Testing the grades_update trigger
+
+-- Initially, there should be 0 records in the history
+SELECT * FROM grades_history;
+
+-- Check the grades
+SELECT * FROM grades;
+
+-- Update a grade
+UPDATE grades
+SET grade = 'C'
+WHERE student_id = 1 AND course_id = 1;
+
+-- Now check the history
+SELECT student_id,
+       change_time,
+       course,
+       old_grade,
+       new_grade
+FROM grades_history;
+
+-- Listing 15-21: Creating a temperature_test table
+
+CREATE TABLE temperature_test (
+    station_name varchar(50),
+    observation_date date,
+    max_temp integer,
+    min_temp integer,
+    max_temp_group varchar(40),
+PRIMARY KEY (station_name, observation_date)
+);
+
+-- Listing 15-22: Creating the classify_max_temp() function
+
+CREATE OR REPLACE FUNCTION classify_max_temp()
+    RETURNS trigger AS
+$$
+BEGIN
+    CASE
+       WHEN NEW.max_temp >= 90 THEN
+           NEW.max_temp_group := 'Hot';
+       WHEN NEW.max_temp BETWEEN 70 AND 89 THEN
+           NEW.max_temp_group := 'Warm';
+       WHEN NEW.max_temp BETWEEN 50 AND 69 THEN
+           NEW.max_temp_group := 'Pleasant';
+       WHEN NEW.max_temp BETWEEN 33 AND 49 THEN
+           NEW.max_temp_group :=  'Cold';
+       WHEN NEW.max_temp BETWEEN 20 AND 32 THEN
+           NEW.max_temp_group :=  'Freezing';
+       ELSE NEW.max_temp_group :=  'Inhumane';
+    END CASE;
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Listing 15-23: Creating the temperature_insert trigger
+
+CREATE TRIGGER temperature_insert
+    BEFORE INSERT
+    ON temperature_test
+    FOR EACH ROW
+    EXECUTE PROCEDURE classify_max_temp();
+
+-- Listing 15-24: Inserting rows to test the temperature_update trigger
+
+INSERT INTO temperature_test (station_name, observation_date, max_temp, min_temp)
+VALUES
+    ('North Station', '1/19/2019', 10, -3),
+    ('North Station', '3/20/2019', 28, 19),
+    ('North Station', '5/2/2019', 65, 42),
+    ('North Station', '8/9/2019', 93, 74);
+
+SELECT * FROM temperature_test;
--- a/Chapter_15/README.md
+++ b/Chapter_15/README.md
@ -0,0 +1,9 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 15: Saving Time with Views, Functions, and Triggers:
+
+Explains how to automate database tasks so you can avoid repeating routine work.
+
+
--- a/Chapter_16/README.md
+++ b/Chapter_16/README.md
@ -0,0 +1,8 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 16: Using PostgreSQL from the Command Line
+
+Covers how to use text commands at your computer’s command prompt to connect to your database and run queries.
+
--- a/Chapter_16/psql_commands.txt
+++ b/Chapter_16/psql_commands.txt
@ -0,0 +1,88 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 16 Code Examples
+--------------------------------------------------------------
+
+
+-- Connecting psql to a database on a local server
+
+psql -d [database name] -U [username]
+psql -d analysis -U postgres
+
+-- Changing user and database name
+
+\c [database name] [user name]
+\c gis_analysis postgres
+
+-- Listing 16-1: 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
+-- Type each line separately, followed by Enter
+
+SELECT geo_name
+FROM us_counties_2010
+LIMIT 3;
+
+
+-- Listing 16-3: Showing open parentheses in the psql prompt
+
+CREATE TABLE wineries (
+id bigint,
+winery_name varchar(100)
+);
+
+-- Listing 16-4: A query with scrolling results
+
+SELECT geo_name FROM us_counties_2010;
+
+-- Listings 16-5 and 16-6: Normal and expanded displays of results
+-- Use \x to toggle expanded on/off
+
+SELECT * FROM grades;
+
+-- Listing 16-7: Importing data using \copy
+
+DROP TABLE state_regions;
+
+CREATE TABLE state_regions (
+    st varchar(2) CONSTRAINT st_key PRIMARY KEY,
+    region varchar(20) NOT NULL
+);
+
+\copy state_regions FROM 'C:\YourDirectory\state_regions.csv' WITH (FORMAT CSV, HEADER);
+
+-- Listing 16-8: Saving query output to a file
+
+-- Enter psql settings
+\a \f , \pset footer
+
+-- This will be the query
+SELECT * FROM grades;
+
+-- Set psql to output results
+-- Note that Windows users must suppply forward slashes for
+-- this command, which is opposite of normal use.
+\o 'C:/YourDirectory/query_output.csv'
+
+-- Run the query and output
+SELECT * FROM grades;
+
+
+-- createdb: Create a database named box_office
+
+createdb -U postgres -e box_office
+
+
+-- Loading shapefiles into PostgreSQL
+
+-- For the US Census county shapefile in Chapter 14:
+shp2pgsql -I -s 4269 -W Latin1 tl_2010_us_county10.shp us_counties_2010_shp | psql -d gis_analysis -U postgres
+
+-- For the Santa Fe roads and waterways shapfiles in Chapter 14:
+shp2pgsql -I -s 4269 tl_2016_35049_roads.shp santafe_roads_2016 | psql -d gis_analysis -U postgres
+shp2pgsql -I -s 4269 tl_2016_35049_linearwater.shp santafe_linearwater_2016 | psql -d gis_analysis -U postgres
--- a/Chapter_17/Chapter_17.sql
+++ b/Chapter_17/Chapter_17.sql
@ -0,0 +1,104 @@
+--------------------------------------------------------------
+-- Practical SQL: A Beginner's Guide to Storytelling with Data
+-- by Anthony DeBarros
+
+-- Chapter 17 Code Examples
+--------------------------------------------------------------
+
+-- VACUUM
+
+-- Listing 17-1: Creating a table to test vacuuming
+
+CREATE TABLE vacuum_test (
+    integer_column integer
+);
+
+-- Listing 17-2: Determining the size of vacuum_test
+
+SELECT pg_size_pretty(
+           pg_total_relation_size('vacuum_test')
+       );
+
+-- optional: Determine database size   
+SELECT pg_size_pretty(
+           pg_database_size('analysis')
+       );
+
+-- Listing 17-3: Inserting 500,000 rows into vacuum_test
+
+INSERT INTO vacuum_test
+SELECT * FROM generate_series(1,500000);
+
+-- Test its size again
+SELECT pg_size_pretty(
+           pg_table_size('vacuum_test')
+       );
+
+-- Listing 17-4: Updating all rows in vacuum_test
+
+UPDATE vacuum_test
+SET integer_column = integer_column + 1;
+
+-- Test its size again (35 MB)
+SELECT pg_size_pretty(
+           pg_table_size('vacuum_test')
+       );
+
+-- Listing 17-5: Viewing autovacuum statistics for vacuum_test
+
+SELECT relname,
+       last_vacuum,
+       last_autovacuum,
+       vacuum_count,
+       autovacuum_count
+FROM pg_stat_all_tables
+WHERE relname = 'vacuum_test';
+
+-- To see all columns available
+SELECT *
+FROM pg_stat_all_tables
+WHERE relname = 'vacuum_test';
+
+-- Listing 17-6: Running VACUUM manually
+
+VACUUM vacuum_test;
+
+VACUUM; -- vacuums the whole database
+
+VACUUM VERBOSE; -- provides messages
+
+-- Listing 17-7: Using VACUUM FULL to reclaim disk space
+
+VACUUM FULL vacuum_test;
+
+-- Test its size again
+SELECT pg_size_pretty(
+           pg_table_size('vacuum_test')
+       );
+       
+-- SETTINGS
+
+-- Listing 17-8: Showing the location of postgresql.conf
+
+SHOW config_file;
+
+-- Listing 17-10: Show the location of the data directory
+
+SHOW data_directory;
+
+-- reload settings
+-- Mac and Linux: pg_ctl reload -D '/path/to/data/directory/'
+-- Windows: pg_ctl reload -D "C:\path\to\data\directory\"
+
+
+-- BACKUP AND RESTORE
+
+-- Listing 17-11: 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
+
+pg_restore -C -d postgres -U postgres analysis_backup_custom.sql
--- a/Chapter_17/README.md
+++ b/Chapter_17/README.md
@ -0,0 +1,8 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Chapter 17: Maintaining Your Database:
+
+Provides tips and procedures for tracking the size of your database, customizing settings, and backing up data.
+
--- a/LICENSE.md
+++ b/LICENSE.md
@ -0,0 +1,11 @@
+### SOFTWARE LICENSE
+
+Copyright (c) 2018 Anthony DeBarros
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software, data, and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+This license shall apply solely to the Software (including data and associated documentation) published at https://github.com/anthonydb/practical-sql/. To remove doubt, this license shall not apply to the surrounding and/or accompanying words, titles, images, graphics, charts, descriptions, tables, indexes, table of contents, and other non-Software matter included in the print and electronic versions of the book "Practical SQL: A Beginner's Guide to Storytelling with Data" (No Starch Press, 2018).
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
--- a/README.md
+++ b/README.md
@ -1,2 +1,57 @@
-# practical-sql-2e
-Practical SQL, 2nd Edition
+## Practical SQL: A Beginner's Guide to Storytelling with Data
+
+[Practical SQL](https://www.nostarch.com/practicalSQL) from No Starch Press is a beginner-friendly guide to the database programming language SQL. Journalist and data analyst  [Anthony DeBarros](https://www.anthonydebarros.com) begins with SQL basics, using the free open-source database PostgreSQL and interface pgAdmin, and walks through intermediate and advanced topics including statistics, aggregation, cleaning data, GIS and automating tasks. Along the way, you'll use real-world data from the U.S. Census and other government agencies and learn the fundamentals of good database design. This book is ideal for beginners as well as those who know some SQL and want to go deeper.
+
+Practical SQL is [available in PDF, .mobi, .epub, and classic print formats](https://www.nostarch.com/practicalSQL).
+
+Questions? Please email [practicalsqlbook@gmail.com](mailto:practicalsqlbook@gmail.com)
+
+## What's Here
+
+**Code**: All the SQL statements and command-line listings used in each chapter, organized by chapter folders.
+
+**Data**: CSV files and GIS shapefiles for you to import, also organized by chapter. **NOTE!** See the warning below about opening CSV files with Excel or text editors in the section on Getting the Code and Data.
+
+**Exercises**: The "Try It Yourself" questions and answers for each chapter, listed separately. Try working through the questions before peeking at the answers!
+
+**FAQ, Updates, and Errata**: Answers to frequently asked questions,  updates, and corrections are noted at [faq-updates-errata.md](https://github.com/anthonydb/practical-sql/blob/master/faq-updates-errata.md).
+
+**Resources**: Updates to the book's Appendix on Additional PostgreSQL Resources at [resources.md](https://github.com/anthonydb/practical-sql/blob/master/resources.md).
+
+## Chapters
+
+* Chapter 1: Creating Your First Database and Table
+* Chapter 2: Beginning Data Exploration with SELECT
+* Chapter 3: Understanding Data Types
+* Chapter 4: Importing and Exporting Data
+* Chapter 5: Basic Math and Stats with SQL
+* Chapter 6: Joining Tables in a Relational Database
+* Chapter 7: Table Design That Works for You
+* Chapter 8: Extracting Information by Grouping and Summarizing
+* Chapter 9: Inspecting and Modifying Data
+* Chapter 10: Statistical Functions In SQL
+* Chapter 11: Working With Dates and Times
+* Chapter 12: Advanced Query Techniques
+* Chapter 13: Mining Text to Find Meaningful Data
+* Chapter 14: Analyzing Spatial Data with PostGIS
+* Chapter 15: Saving Time with Views, Functions, and Triggers
+* Chapter 16: Using PostgreSQL from the Command Line
+* Chapter 17: Maintaining Your Database
+* Chapter 18: Identifying and Telling the Story Behind Your Data
+* Appendix: Additional PostgreSQL Resources
+
+## Getting the Code and Data on Your Computer
+
+**Non-GitHub Users**
+
+You can obtain all the code and data at once by downloading this repository as a .zip file. To do that:
+
+* Select the **Clone or Download** button at top right.
+* Select **Download ZIP**
+* Unzip the file on your computer. Place it in a directory that's easy to remember so you can reference it during the exercises that include importing data to PostgreSQL.
+
+**Warning about CSV files!**: Opening CSV files with Excel could lead to data loss. Excel will remove leading zeros from numbers that are intended to be stored as text, such as zip codes. If you wish to view the contents of a CSV file, only do so with a plain-text editor and be careful not to save the file in an encoding other than UTF-8 (e.g., `Notepad.exe` on Windows defaults to ANSI).
+
+**GitHub Users**
+
+GitHub users may want to clone the repository locally and occasionally perform a `git pull` to receive any updates.
--- a/Try_It_Yourself/README.md
+++ b/Try_It_Yourself/README.md
@ -0,0 +1,5 @@
+## Practical SQL
+
+[PracticalSQL](https://www.nostarch.com/practicalSQL) by Anthony DeBarros is [available from No Starch Press](https://www.nostarch.com/practicalSQL).
+
+### Try It Yourself Answers
--- a/Try_It_Yourself/Try_It_Yourself.sql
+++ b/Try_It_Yourself/Try_It_Yourself.sql
--- a/faq-updates-errata.md
+++ b/faq-updates-errata.md
@ -0,0 +1,84 @@
+## Practical SQL: A Beginner's Guide to Storytelling with Data
+
+### FAQ, Updates, and Errata
+
+This page contains answers to Frequently Asked Questions, additional information, and errata related to material in Practical SQL.
+
+### Introduction
+
+#### Working with pgAdmin: Changes to pgAdmin app
+
+**Browser app:** Figure 1 on page xxxiii of the Introduction shows the pgAdmin app's opening screen. Beginning with version 3.0 of pgAdmin, released in April 2018, the application loads as a tab within your default web browser. Previous versions of pgAdmin run as a standalone desktop application. All other functionality for pgAdmin described in the book remains the same. Early copies of the book show pgAdmin as a standalone app. This has been updated in more recent printings. 
+
+**Master password:** Beginning with version 4.7 of pgAdmin, released in May 2019, the application asks you to set a "master password" that's used in addition to the database password set on installation.
+
+**Execute/refresh button icon:** Beginning with version 4.15 of pgAdmin, the icon for the button that executes a SQL statement was changed from a lightning bolt to a "play" symbol.
+
+### Chapter 1: Creating Your First Database and Table
+
+**Updated execute/refresh button icon:**
+
+Step 7 on page 4 and Step 3 on page 7 both refer to clicking a lightning bolt icon in pgAdmin to execute your SQL statements. Starting with version 4.15 of pgAdmin, the icon for the button that executes a SQL statement was changed from a lightning bolt to a "play" symbol.
+
+### Chapter 4: Importing and Exporting Data
+
+**Avoiding common import errors**:
+
+Be sure to download the data files and code examples from this repo using the steps outlined on page xxvii of the book's Introduction. That is, on the [main GitHub page for this repo](https://github.com/anthonydb/practical-sql), click the `Clone or Download` button and download a ZIP file with all the materials. Trying to copy/paste from GitHub often creates problems because HTML coding can get added to the data.
+
+In addition, avoid opening or changing CSV files with Excel or a text editor, which can lead to data loss or encoding changes. See page 135 of the book for details.
+
+### Chapter 9: Inspecting and Modifying Data
+
+**Updated data file**:
+
+As of February 24, 2019, this repository contains a revised version of the CSV file `MPI_Directory_by_Establishment_Name.csv'`. The only difference between it and the original is the format of the dates in the `grant_date` column. They are now in ISO 8601 international standard format, `YYYY-MM-DD`. I made the change to accommodate international translation of Practical SQL. This change affects none of the exercises. If you would like to use the original CSV for some reason, I moved it to the [Misc folder](https://github.com/anthonydb/practical-sql/blob/master/Misc/).
+
+### Chapter 11: Working With Dates and Times
+
+**New data dictionary URL for taxi trip times data**
+
+Page 182: The PDF of the data dictionary describing the columns and codes for the New York City taxi data has moved. It is now at [https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)
+
+### Chapter 12: Advanced Query Techniques
+
+**Errata**:
+
+Page 195: The results shown for Listing 12-3 are correct. However, the first sentence after the results has an incorrect number. It should read, "The difference between the median and average, 72,376, is nearly three times the size of the median."
+
+### Chapter 13: Mining Text to Find Meaningful Data
+
+**Errata**:
+
+Page 230: The final line in Listing 13-13 should have a space instead of a comma between the quote marks in the second argument. It should read:
+
+`SELECT regexp_split_to_array('Phil Mike Tony Steve', ' ');`
+
+It's correct in the code listing on GitHub [here](https://github.com/anthonydb/practical-sql/blob/master/Chapter_13/Chapter_13.sql#L223).
+
+Page 238: The final line of Listing 13-25 is missing a semi-colon. It should read:
+
+`LIMIT 5;`
+
+It's correct in the code listing on GitHub [here](https://github.com/anthonydb/practical-sql/blob/master/Chapter_13/Chapter_13.sql#L349).
+
+### Chapter 14: Analyzing Spatial Data with PostGIS
+
+#### macOS PostGIS Shapefile and DBF Loader Exporter Unavailable
+
+As noted on page 257 of the book, the shapefile GUI tool that's available for Windows and Linux is unfortunately no longer maintained for macOS. If that changes, I'll note here. In the meantime, use the `shp2pgsql` utility covered in Chapter 16 to load shapefiles into your database. The commands for both the Census county data and the New Mexico roads and waterways are [here](https://github.com/anthonydb/practical-sql/blob/master/Chapter_16/psql_commands.txt#L81).
+
+### Chapter 15: Saving Time with Views, Functions, and Triggers
+
+#### PL/Python Extension Windows Installation Error
+
+When attempting to run the command `CREATE EXTENSION plpythonu;` from Listing 15-14 on page 281, Windows users may receive the error `could not access file "$libdir/plpython2": No such file or directory`. This means PostgreSQL was unable to find the necessary Python language files on your system.
+
+Upon investigation, I discovered that the file `plpython3.dll` included with PostgreSQL during the EnterpriseDB Windows installation is looking for the file `python34.dll` to be present in the `C:\Windows\System32` directory. This file is included with the EDB Language Pack but not placed in that directory.
+
+Here's how to remedy the situation. Note that you must have installed the EDB Language Pack as described on page xxx of the introduction to "Practical SQL":
+
+* Using your File Explorer, navigate to `C:\edb\languagepack-10\x64\Python-3.4`
+* Copy the file `python34.dll` (right-click and select Copy).
+* Using File Explorer, navigate to `C:\Windows\System32` and paste the file.
+* You then should be able to execute the command `CREATE EXTENSION plpython3u;` within your database. Note that this command is slightly different than in the book. You're naming `plpython3u` instead of `plpythonu`.
--- a/resources.md
+++ b/resources.md
@ -0,0 +1,69 @@
+## Practical SQL: A Beginner's Guide to Storytelling with Data
+
+[Practical SQL](https://www.nostarch.com/practicalsql/) by Anthony DeBarros is available from No Starch Press.
+
+### Resources
+
+The Appendix of Practical SQL contains a list of resources to help you stay informed about PostgreSQL developments, find additional software, and get help. Below is an ongoing list updated from the original in the book. If you'd like to suggest an addition or edit, please submit an issue ticket.
+
+### PostgreSQL Development Environments
+
+Throughout the book, we’ve used the graphical user interface [pgAdmin](https://www.pgadmin.org) to connect to PostgreSQL, run queries, and view database objects. Although pgAdmin is free, open source, and popular, it’s not your only choice for working with PostgreSQL. You can read the entry called “Community Guide to PostgreSQL GUI Tools,” which catalogs many alternatives, on the PostgreSQL wiki at [https://wiki.postgresql.org/wiki/Community_Guide_to_PostgreSQL_GUI_Tools](https://wiki.postgresql.org/wiki/Community_Guide_to_PostgreSQL_GUI_Tools).
+
+The following list contains information on several tools I’ve tried, including free and paid options. The free tools work well for general analysis work. But if you wade deeper into database development, you might want to upgrade to the paid options, which typically offer advanced features and support:
+
+[DataGrip](http://www.jetbrains.com/datagrip/): A SQL development environment that offers code completion, bug detection, and suggestions for streamlining code, among many other features. It’s a paid product, but the company, JetBrains, offers discounts and free versions for students, educators, and nonprofits.
+
+[Navicat](https://www.navicat.com/): A richly featured SQL development environment with ver- sions that support PostgreSQL as well as other databases, including MySQL, Oracle, and Microsoft SQL Server. Navicat is a paid version only, but the company offers a 14-day free trial.
+
+[pgManage](https://github.com/pgManage/pgManage/): A free, open source GUI client for Windows, macOS, and Linux, formerly known as Postage.
+
+[Postico](https://eggerapps.at/postico/): A macOS-only client from the maker of Postgres.app that looks like it takes its cues from Apple design. The full version is paid, but a restricted-feature version is available with no time limit.
+
+[PSequel](http://www.psequel.com/): Also macOS-only, PSequel is a free PostgreSQL client that is decidedly minimalist.
+
+A trial version can help you decide whether the product is right for you.
+
+### PostgreSQL Utilities, Tools, and Extensions
+
+You can expand the capabilities of PostgreSQL via numerous third-party utilities, tools, and extensions. These range from additional backup and import/export options to improved formatting for the command line to powerful statistics packages. You’ll find a curated list at [https://github.com/dhamaniasad/awesome-postgres/](https://github.com/dhamaniasad/awesome-postgres/), but here are several to highlight:
+
+[Devart Excel Add-In for PostgreSQL](https://www.devart.com/excel-addins/postgresql.html): An add-in that lets you load and edit data from PostgreSQL directly in Excel workbooks.
+
+[MADlib](http://madlib.apache.org/): A machine learning and analytics library for large data sets.
+
+[pgAgent](https://www.pgadmin.org/docs/pgadmin4/dev/pgagent.html): A job manager that lets you run queries at scheduled times, among other tasks.
+
+[pgcli](https://github.com/dbcli/pgcli/): A replacement for psql that includes improved formatting when writing queries and viewing output.
+
+[PL/R](http://www.joeconway.com/plr.html): A loadable procedural language that provides the ability to use the R statistical programming language within PostgreSQL functions and triggers.
+
+[SciPy](https://www.scipy.org/): A collection of Python science and engineering libraries you can use with the PL/Python procedural language in PostgreSQL.
+
+[TimescaleDB](https://www.timescale.com/): A PostgreSQL extension that specializes in handling time-series data. 
+
+### PostgreSQL News
+
+Here’s a collection of online resources you can use to stay informed:
+
+[EDB Blog](https://www.enterprisedb.com/blog/): Posts from the team at EnterpriseDB, a PostgreSQL services company that provides the Windows installer referenced in this book.
+
+[Planet PostgreSQL](https://planet.postgresql.org/): A collection of blog posts and announcements from the database community.
+
+[Postgres Weekly](https://postgresweekly.com/): An email newsletter that rounds up announcements, blog posts, and product announcements.
+
+[PostgreSQL Mailing Lists](https://www.postgresql.org/list/): These lists are useful for asking questions of community experts. The pgsql-novice and pgsql-general lists are particularly good for beginners, although note that email volume can be heavy.
+
+[PostgreSQL News Archive](https://www.postgresql.org/about/newsarchive/): Official news from the Postgres team.
+
+[PostGIS Blog](http://postgis.net/blog/): Announcements and updates on the PostGIS extension covered in Chapter 14.
+
+Additionally, I recommend paying attention to developer notes for any of the PostgreSQL-related software you use, such as pgAdmin.
+
+### Documentation
+
+You can find documentation for each version of PostgreSQL along with an FAQ and wiki on the [main page](https://www.postgresql.org/docs/). It’s worth reading through various sections of the manual as you learn more about a particular topic, such as indexes, or search for all the options that come with functions. In particular, the Preface, Tutorial, and SQL Language sections cover much of the material presented in the book’s chapters.
+
+Other good resources for documentation are the [Postgres Guide](http://postgresguide.com/) and [Stack Overflow](https://stackoverflow.com/questions/tagged/postgresql/), where you can find questions and answers posted by developers. You can also check out the Q&A site for [PostGIS](https://gis.stackexchange.com/questions/tagged/postgis/.
+
+
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