355 lines
9.8 KiB
SQL
355 lines
9.8 KiB
SQL
---------------------------------------------------------------------------
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-- Practical SQL: A Beginner's Guide to Storytelling with Data, 2nd Edition
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-- by Anthony DeBarros
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-- Chapter 13 Code Examples
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----------------------------------------------------------------------------
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-- Listing 13-1: Using a subquery in a WHERE clause
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SELECT county_name,
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state_name,
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pop_est_2019
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FROM us_counties_pop_est_2019
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WHERE pop_est_2019 >= (
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SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY pop_est_2019)
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FROM us_counties_pop_est_2019
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)
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ORDER BY pop_est_2019 DESC;
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-- Listing 13-2: Using a subquery in a WHERE clause for DELETE
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CREATE TABLE us_counties_2019_top10 AS
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SELECT * FROM us_counties_pop_est_2019;
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DELETE FROM us_counties_2019_top10
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WHERE pop_est_2019 < (
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SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY pop_est_2019)
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FROM us_counties_2019_top10
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);
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SELECT count(*) FROM us_counties_2019_top10;
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-- Listing 13-3: Subquery as a derived table in a FROM clause
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SELECT round(calcs.average, 0) as average,
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calcs.median,
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round(calcs.average - calcs.median, 0) AS median_average_diff
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FROM (
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SELECT avg(pop_est_2019) AS average,
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percentile_cont(.5)
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WITHIN GROUP (ORDER BY pop_est_2019)::numeric AS median
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FROM us_counties_pop_est_2019
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)
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AS calcs;
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-- Listing 13-4: Joining two derived tables
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SELECT census.state_name AS st,
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census.pop_est_2018,
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est.establishment_count,
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round((est.establishment_count/census.pop_est_2018::numeric) * 1000, 1)
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AS estabs_per_thousand
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FROM
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(
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SELECT st,
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sum(establishments) AS establishment_count
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FROM cbp_naics_72_establishments
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GROUP BY st
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)
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AS est
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JOIN
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(
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SELECT state_name,
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sum(pop_est_2018) AS pop_est_2018
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FROM us_counties_pop_est_2019
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GROUP BY state_name
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)
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AS census
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ON est.st = census.state_name
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ORDER BY estabs_per_thousand DESC;
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-- Listing 13-5: Adding a subquery to a column list
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SELECT county_name,
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state_name AS st,
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pop_est_2019,
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(SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY pop_est_2019)
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FROM us_counties_pop_est_2019) AS us_median
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FROM us_counties_pop_est_2019;
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-- Listing 13-6: Using a subquery in a calculation
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SELECT county_name,
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state_name AS st,
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pop_est_2019,
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pop_est_2019 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY pop_est_2019)
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FROM us_counties_pop_est_2019) AS diff_from_median
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FROM us_counties_pop_est_2019
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WHERE (pop_est_2019 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY pop_est_2019)
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FROM us_counties_pop_est_2019))
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BETWEEN -1000 AND 1000;
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-- Listing 13-7: Creating and filling a retirees table
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CREATE TABLE retirees (
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id int,
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first_name text,
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last_name text
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);
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INSERT INTO retirees
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VALUES (2, 'Janet', 'King'),
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(4, 'Michael', 'Taylor');
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-- Listing 13-8: Generating values for the IN operator
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SELECT first_name, last_name
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FROM employees
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WHERE emp_id IN (
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SELECT id
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FROM retirees);
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-- Listing 13-9: Using a correlated subquery with WHERE EXISTS
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SELECT first_name, last_name
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FROM employees
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WHERE EXISTS (
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SELECT id
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FROM retirees
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WHERE id = employees.emp_id);
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-- Listing 13-10: Using a correlated subquery with WHERE NOT EXISTS
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SELECT first_name, last_name
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FROM employees
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WHERE NOT EXISTS (
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SELECT id
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FROM retirees
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WHERE id = employees.emp_id);
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-- Listing 13-11: Using LATERAL subqueries in the FROM clause
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SELECT county_name,
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state_name,
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pop_est_2018,
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pop_est_2019,
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raw_chg,
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round(pct_chg * 100, 2) AS pct_chg
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FROM us_counties_pop_est_2019,
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LATERAL (SELECT pop_est_2019 - pop_est_2018 AS raw_chg) rc,
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LATERAL (SELECT raw_chg / pop_est_2018::numeric AS pct_chg) pc
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ORDER BY pct_chg DESC;
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-- Listing 13-12: Using a subquery with a LATERAL join
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ALTER TABLE teachers ADD CONSTRAINT id_key PRIMARY KEY (id);
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CREATE TABLE teachers_lab_access (
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access_id bigint PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
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access_time timestamp with time zone,
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lab_name text,
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teacher_id bigint REFERENCES teachers (id)
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);
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INSERT INTO teachers_lab_access (access_time, lab_name, teacher_id)
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VALUES ('2022-11-30 08:59:00-05', 'Science A', 2),
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('2022-12-01 08:58:00-05', 'Chemistry B', 2),
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('2022-12-21 09:01:00-05', 'Chemistry A', 2),
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('2022-12-02 11:01:00-05', 'Science B', 6),
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('2022-12-07 10:02:00-05', 'Science A', 6),
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('2022-12-17 16:00:00-05', 'Science B', 6);
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SELECT t.first_name, t.last_name, a.access_time, a.lab_name
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FROM teachers t
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LEFT JOIN LATERAL (SELECT *
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FROM teachers_lab_access
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WHERE teacher_id = t.id
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ORDER BY access_time DESC
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LIMIT 2) a
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ON true
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ORDER BY t.id;
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-- Common Table Expressions
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-- Listing 13-13: Using a simple CTE to count large counties
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WITH large_counties (county_name, state_name, pop_est_2019)
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AS (
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SELECT county_name, state_name, pop_est_2019
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FROM us_counties_pop_est_2019
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WHERE pop_est_2019 >= 100000
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)
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SELECT state_name, count(*)
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FROM large_counties
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GROUP BY state_name
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ORDER BY count(*) DESC;
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-- Bonus: You can also write this query as:
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SELECT state_name, count(*)
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FROM us_counties_pop_est_2019
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WHERE pop_est_2019 >= 100000
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GROUP BY state_name
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ORDER BY count(*) DESC;
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-- Listing 13-14: Using CTEs in a table join
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WITH
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counties (st, pop_est_2018) AS
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(SELECT state_name, sum(pop_est_2018)
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FROM us_counties_pop_est_2019
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GROUP BY state_name),
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establishments (st, establishment_count) AS
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(SELECT st, sum(establishments) AS establishment_count
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FROM cbp_naics_72_establishments
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GROUP BY st)
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SELECT counties.st,
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pop_est_2018,
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establishment_count,
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round((establishments.establishment_count /
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counties.pop_est_2018::numeric(10,1)) * 1000, 1)
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AS estabs_per_thousand
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FROM counties JOIN establishments
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ON counties.st = establishments.st
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ORDER BY estabs_per_thousand DESC;
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-- Listing 13-15: Using CTEs to minimize redundant code
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WITH us_median AS
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(SELECT percentile_cont(.5)
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WITHIN GROUP (ORDER BY pop_est_2019) AS us_median_pop
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FROM us_counties_pop_est_2019)
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SELECT county_name,
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state_name AS st,
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pop_est_2019,
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us_median_pop,
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pop_est_2019 - us_median_pop AS diff_from_median
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FROM us_counties_pop_est_2019 CROSS JOIN us_median
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WHERE (pop_est_2019 - us_median_pop)
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BETWEEN -1000 AND 1000;
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-- Cross tabulations
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-- Install the crosstab() function via the tablefunc module
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CREATE EXTENSION tablefunc;
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-- Listing 13-16: Creating and filling the ice_cream_survey table
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CREATE TABLE ice_cream_survey (
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response_id integer PRIMARY KEY,
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office text,
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flavor text
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);
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COPY ice_cream_survey
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FROM 'C:\YourDirectory\ice_cream_survey.csv'
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WITH (FORMAT CSV, HEADER);
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-- view the data
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SELECT *
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FROM ice_cream_survey
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ORDER BY response_id
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LIMIT 5;
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-- Listing 13-17: Generating the ice cream survey crosstab
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SELECT *
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FROM crosstab('SELECT office,
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flavor,
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count(*)
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FROM ice_cream_survey
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GROUP BY office, flavor
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ORDER BY office',
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'SELECT flavor
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FROM ice_cream_survey
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GROUP BY flavor
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ORDER BY flavor')
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AS (office text,
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chocolate bigint,
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strawberry bigint,
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vanilla bigint);
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-- Listing 13-18: Creating and filling a temperature_readings table
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CREATE TABLE temperature_readings (
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station_name text,
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observation_date date,
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max_temp integer,
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min_temp integer,
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CONSTRAINT temp_key PRIMARY KEY (station_name, observation_date)
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);
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COPY temperature_readings
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FROM 'C:\YourDirectory\temperature_readings.csv'
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WITH (FORMAT CSV, HEADER);
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-- Listing 13-19: Generating the temperature readings crosstab
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SELECT *
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FROM crosstab('SELECT
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station_name,
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date_part($$month$$, observation_date),
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percentile_cont(.5)
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WITHIN GROUP (ORDER BY max_temp)
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FROM temperature_readings
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GROUP BY station_name,
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date_part(''month'', observation_date)
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ORDER BY station_name',
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'SELECT month
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FROM generate_series(1,12) month')
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AS (station text,
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jan numeric(3,0),
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feb numeric(3,0),
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mar numeric(3,0),
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apr numeric(3,0),
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may numeric(3,0),
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jun numeric(3,0),
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jul numeric(3,0),
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aug numeric(3,0),
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sep numeric(3,0),
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oct numeric(3,0),
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nov numeric(3,0),
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dec numeric(3,0)
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);
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-- Listing 13-20: Re-classifying temperature data with CASE
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SELECT max_temp,
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CASE WHEN max_temp >= 90 THEN 'Hot'
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WHEN max_temp >= 70 AND max_temp < 90 THEN 'Warm'
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WHEN max_temp >= 50 AND max_temp < 70 THEN 'Pleasant'
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WHEN max_temp >= 33 AND max_temp < 50 THEN 'Cold'
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WHEN max_temp >= 20 AND max_temp < 33 THEN 'Frigid'
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WHEN max_temp < 20 THEN 'Inhumane'
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ELSE 'No reading'
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END AS temperature_group
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FROM temperature_readings;
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-- Listing 13-21: Using CASE in a Common Table Expression
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WITH temps_collapsed (station_name, max_temperature_group) AS
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(SELECT station_name,
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CASE WHEN max_temp >= 90 THEN 'Hot'
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WHEN max_temp >= 70 AND max_temp < 90 THEN 'Warm'
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WHEN max_temp >= 50 AND max_temp < 70 THEN 'Pleasant'
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WHEN max_temp >= 33 AND max_temp < 50 THEN 'Cold'
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WHEN max_temp >= 20 AND max_temp < 33 THEN 'Frigid'
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WHEN max_temp < 20 THEN 'Inhumane'
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ELSE 'No reading'
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END
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FROM temperature_readings)
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SELECT station_name, max_temperature_group, count(*)
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FROM temps_collapsed
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GROUP BY station_name, max_temperature_group
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ORDER BY station_name, count(*) DESC;
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