📰 Summary (use your own words)

SQL code for fast data analysis so one can skip using Pandas if not needed. This article is showcasing PostgreSQL.

✍️ Notes

• There is a high cost in loading data into Pandas, so it is often not worth it for ad-hoc analysis
  • Or it is worth pre-analysis in SQL itself before fetching it into Pandas

Common Table Expressions (CTE)

• Good for recursive queries
• Good for readability and modularity

WITH emails AS (
	SELECT 'PAUL.DENG@test.com' AS email
), 
normalized_emails AS ( 
	SELECT lower(email) as email FROM emails
)
SELECT * FROM normalized_emails;

email
paul.deng@test.com

Generating Data

• Useful for joining data to
• Can use UNION ALL to concat data, VALUE LIST to create constant data, UNNEST to generate small 1 dimensional data from a list
• A really useful command is to use GENERATE_SERIES for large amounts of data with a fixed step and can be used for time series

WITH daterange AS (
	SELECT *
	FROM generate_series(
		'2023-01-01 UTC'::timestamptz, --start
		'2023-01-02 UTC'::timestamptz, --end
		INTERVAL '1 hour' --step
	) WITH ORDINALITY AS t(hh, n)
)
SELECT * FROM daterange

Sampling

• To produce random numbers, one can use CEIL(RANDOM()) or FLOOR(RANDOM())
  • Don’t use ROUND(RANDOM()) because it doesn’t retain the distribution

SELECT 
	CEIL(RANDOM() * 3) AS n,
	COUNT(*)
FROM generate_series(0, 1000)
GROUP BY 1

• One can extend this to produce random choice

SELECT 
	(array['RED', 'BLUE', 'GREEN'])[CEIL(RANDOM() * 3)] AS color 
FROM generate_series(1, 5)

• To sample you can use RANDOM() but it is pretty inefficient
• So PostgreSQL has two methods: system and bernoulli
  • system is done with blocks so it is faster

-- using random
WITH random_sample AS ( 
	SELECT * 
	FROM users 
	ORDER BY RANDOM() LIMIT 1000
)
SELECT COUNT(*) FROM random_sample

-- using system
WITH system_sample AS (
	SELECT *
	FROM users TABLESAMPLE() SYSTEM(10) -- argument is percentage
)
SELECT COUNT(*) FROM system_sample

-- using bernoulli 
WITH bernoulli_sample AS (
	SELECT *
	FROM users TABLESAMPLE() BERNOULLI(10) -- argument is percentage
)
SELECT COUNT(*) FROM bernoulli_sample

Data Exploration

• Can calculate simple statistical descriptions with COUNT, AVG, STDDEV, MIN, MAX, MODE and PERCENTILE_CONT or PERCENTILE_DISC
• The median of a series can be calculated with either percentile functions
  • PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY n) gives a value that 50% of the values are less than which could be a value that doesn’t exist in the series
  • PERCENTILE_DISC(0.5) WITHIN GROUP (ORDER BY n) gives the value that 50% of the values are less than which is from the series

Subtotals

• There is a ROLLUP command that gives hierarchical subtotals, but only for the combination specified

WITH emp AS (
	SELECT * FROM (VALUES
		('Haki', 'R&D', 'Manager'),
		('Dan', 'R&D', 'Developer'),
		('Jax', 'R&D', 'Developer'),
		('George', 'Sales', 'Manager'),
		('Bill', 'Sales', 'Developer'),
		('David', 'Sales', 'Developer')
	) AS t(
	 name, department, role
	)
)
SELECT department, role, COUNT(*)
FROM emp
GROUP BY ROLLUP(department), role;

• To get all combinations, we can precalculate them using CUBE
• Both of these commands are just syntax sugar for grouping sets which gives the explicit combinations

Pivot Tables

• We can also reshape data in SQL by using conditional expressions CASE ... WHEN ... THEN .. WHEN ... THEN ... ELSE ... END
• Another way to do this is through aggregate expressions COUNT(*) FILTER (WHERE ...) AS
• Overall, it is still tedious to do compared to pandas so if there are lots of transformations being done it might be better to use pandas here

Cumulative Aggregation

• Use the window function OVER (PARTITION BY ...) and passing that into a statistical function
  • The partition by command can take an integer to evaluate over all the rows
  • It can also take a frame clause to perform moving window

Statistics

• PostgreSQL even have built in regression functions like REGR_SLOPE(), REGR_INTERCEPT
• Interpolating missing data or finer granularity is also a common task, PostgreSQL can do this with COALESC to get a default value and more complex query to do forward fill and back fill as well
• Binning with CASE ... WHEN and if we want equal height binning we can use NTILE and histogram (equal width binning) with WIDTH_BUCKET