311 lines
8.5 KiB
SQL
311 lines
8.5 KiB
SQL
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-- Practical SQL: A Beginner's Guide to Storytelling with Data
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-- by Anthony DeBarros
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-- Chapter 12 Code Examples
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--------------------------------------------------------------
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-- Listing 12-1: Using a subquery in a WHERE clause
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SELECT geo_name,
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state_us_abbreviation,
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p0010001
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FROM us_counties_2010
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WHERE p0010001 >= (
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SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY p0010001)
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FROM us_counties_2010
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)
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ORDER BY p0010001 DESC;
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-- Listing 12-2: Using a subquery in a WHERE clause for DELETE
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CREATE TABLE us_counties_2010_top10 AS
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SELECT * FROM us_counties_2010;
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DELETE FROM us_counties_2010_top10
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WHERE p0010001 < (
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SELECT percentile_cont(.9) WITHIN GROUP (ORDER BY p0010001)
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FROM us_counties_2010_top10
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);
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SELECT count(*) FROM us_counties_2010_top10;
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-- Listing 12-3: 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(p0010001) AS average,
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percentile_cont(.5)
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WITHIN GROUP (ORDER BY p0010001)::numeric(10,1) AS median
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FROM us_counties_2010
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)
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AS calcs;
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-- Listing 12-4: Joining two derived tables
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SELECT census.state_us_abbreviation AS st,
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census.st_population,
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plants.plant_count,
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round((plants.plant_count/census.st_population::numeric(10,1)) * 1000000, 1)
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AS plants_per_million
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FROM
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(
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SELECT st,
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count(*) AS plant_count
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FROM meat_poultry_egg_inspect
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GROUP BY st
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)
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AS plants
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JOIN
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(
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SELECT state_us_abbreviation,
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sum(p0010001) AS st_population
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FROM us_counties_2010
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GROUP BY state_us_abbreviation
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)
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AS census
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ON plants.st = census.state_us_abbreviation
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ORDER BY plants_per_million DESC;
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-- Listing 12-5: Adding a subquery to a column list
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SELECT geo_name,
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state_us_abbreviation AS st,
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p0010001 AS total_pop,
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(SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
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FROM us_counties_2010) AS us_median
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FROM us_counties_2010;
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-- Listing 12-6: Using a subquery expression in a calculation
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SELECT geo_name,
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state_us_abbreviation AS st,
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p0010001 AS total_pop,
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(SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
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FROM us_counties_2010) AS us_median,
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p0010001 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
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FROM us_counties_2010) AS diff_from_median
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FROM us_counties_2010
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WHERE (p0010001 - (SELECT percentile_cont(.5) WITHIN GROUP (ORDER BY p0010001)
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FROM us_counties_2010))
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BETWEEN -1000 AND 1000;
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-- BONUS: Subquery expressions
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-- If you'd like to try the IN, EXISTS, and NOT EXISTS expressions on pages 199-200,
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-- here's the code to create a retirees table. The queries below are similar
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-- to the hypothetical examples on pages 199 and 200. You will need the
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-- employees table you created in Chapter 6.
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-- Create table and insert data
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CREATE TABLE retirees (
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id int,
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first_name varchar(50),
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last_name varchar(50)
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);
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INSERT INTO retirees
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VALUES (2, 'Lee', 'Smith'),
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(4, 'Janet', 'King');
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-- 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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-- Checking whether values exist (returns all rows from employees
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-- if the expression evaluates as true)
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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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-- Using a correlated subquery to find matching values from employees
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-- in retirees.
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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 12-7: Using a simple CTE to find large counties
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WITH
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large_counties (geo_name, st, p0010001)
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AS
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(
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SELECT geo_name, state_us_abbreviation, p0010001
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FROM us_counties_2010
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WHERE p0010001 >= 100000
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)
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SELECT st, count(*)
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FROM large_counties
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GROUP BY st
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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_us_abbreviation, count(*)
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FROM us_counties_2010
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WHERE p0010001 >= 100000
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GROUP BY state_us_abbreviation
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ORDER BY count(*) DESC;
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-- Listing 12-8: Using CTEs in a table join
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WITH
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counties (st, population) AS
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(SELECT state_us_abbreviation, sum(population_count_100_percent)
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FROM us_counties_2010
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GROUP BY state_us_abbreviation),
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plants (st, plants) AS
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(SELECT st, count(*) AS plants
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FROM meat_poultry_egg_inspect
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GROUP BY st)
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SELECT counties.st,
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population,
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plants,
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round((plants/population::numeric(10,1))*1000000, 1) AS per_million
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FROM counties JOIN plants
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ON counties.st = plants.st
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ORDER BY per_million DESC;
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-- Listing 12-9: 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 p0010001) AS us_median_pop
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FROM us_counties_2010)
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SELECT geo_name,
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state_us_abbreviation AS st,
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p0010001 AS total_pop,
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us_median_pop,
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p0010001 - us_median_pop AS diff_from_median
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FROM us_counties_2010 CROSS JOIN us_median
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WHERE (p0010001 - 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 12-10: 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 varchar(20),
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flavor varchar(20)
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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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-- Listing 12-11: 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 varchar(20),
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chocolate bigint,
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strawberry bigint,
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vanilla bigint);
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-- Listing 12-12: Creating and filling a temperature_readings table
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CREATE TABLE temperature_readings (
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reading_id bigserial PRIMARY KEY,
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station_name varchar(50),
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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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);
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COPY temperature_readings
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(station_name, observation_date, max_temp, min_temp)
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FROM 'C:\YourDirectory\temperature_readings.csv'
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WITH (FORMAT CSV, HEADER);
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-- Listing 12-13: 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 varchar(50),
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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 12-14: 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 BETWEEN 70 AND 89 THEN 'Warm'
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WHEN max_temp BETWEEN 50 AND 69 THEN 'Pleasant'
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WHEN max_temp BETWEEN 33 AND 49 THEN 'Cold'
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WHEN max_temp BETWEEN 20 AND 32 THEN 'Freezing'
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ELSE 'Inhumane'
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END AS temperature_group
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FROM temperature_readings;
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-- Listing 12-15: 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 BETWEEN 70 AND 89 THEN 'Warm'
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WHEN max_temp BETWEEN 50 AND 69 THEN 'Pleasant'
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WHEN max_temp BETWEEN 33 AND 49 THEN 'Cold'
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WHEN max_temp BETWEEN 20 AND 32 THEN 'Freezing'
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ELSE 'Inhumane'
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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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