Else do nothing SQL query - sql

I have a field, froiexported, in DB table claim3 that is either set to one or zero. I want to run an update where if the criteria in the case statement is met the value in froiexported is set to 1 else do nothing. Below will make my results incorrect every day.
update claim3
set froiexpoted =
CASE
WHEN froimaintdate >= dateadd(day,datediff(day,1,GETDATE()),0)
AND froimaintdate < dateadd(day,datediff(day,0,GETDATE()),0)
AND c1.jurst in ('AK', 'AL', 'CA', 'CO', 'FL', 'GA', 'IA', 'IN', 'KS', 'KY', 'LA', 'MA', 'ME', 'MN', 'MO', 'MS', 'NC', 'NE', 'NJ', 'PA', 'RI', 'SC', 'TN', 'TX', 'UT', 'VA', 'VT', 'WV')
THEN '1'
ELSE '0'
END


You can use a where clause instead:
update claim3
set froiexpoted = 1
where froiexpoted <> 1
and froimaintdate >= dateadd(day,datediff(day,1,getdate()),0)
and froimaintdate < dateadd(day,datediff(day,0,getdate()),0)
and c1.jurst in ('AK', 'AL', 'CA', 'CO', 'FL', 'GA', 'IA', 'IN'
, 'KS','KY', 'LA', 'MA', 'ME', 'MN', 'MO', 'MS', 'NC', 'NE'
, 'NJ', 'PA', 'RI', 'SC', 'TN', 'TX', 'UT', 'VA', 'VT', 'WV'
)
if you need to set 0s for the previous day as well:
update claim3
set froiexpoted = case
when c1.jurst in ('AK', 'AL', 'CA', 'CO', 'FL', 'GA', 'IA', 'IN'
, 'KS','KY', 'LA', 'MA', 'ME', 'MN', 'MO', 'MS', 'NC', 'NE'
, 'NJ', 'PA', 'RI', 'SC', 'TN', 'TX', 'UT', 'VA', 'VT', 'WV'
)
then 1
else 0
end
where froimaintdate >= dateadd(day,datediff(day,1,getdate()),0)
and froimaintdate < dateadd(day,datediff(day,0,getdate()),0)


How about setting it to 1 if criteria are met, else set to the current value?

Related

Extract words from a column and count frequency

Does anyone know if there's an efficient way to extract all the words from a single column and count the frequency of each word in SQL Server? I only have read-only access to my database so I can't create a self-defined function to do this.
Here's a reproducible example:
CREATE TABLE words
(
id INT PRIMARY KEY,
text_column VARCHAR(1000)
);
INSERT INTO words (id, text_column)
VALUES
(1, 'SQL Server is a popular database management system'),
(2, 'It is widely used for data storage and retrieval'),
(3, 'SQL Server is a powerful tool for data analysis');
I have found this code but it's not working correctly, and I think it's too complicated to understand:
WITH E1(N) AS
(
SELECT 1
FROM (VALUES
(1),(1),(1),(1),(1),(1),(1),(1),(1),(1)
) t(N)
),
E2(N) AS (SELECT 1 FROM E1 a CROSS JOIN E1 b),
E4(N) AS (SELECT 1 FROM E2 a CROSS JOIN E2 b)
SELECT
LOWER(x.Item) AS [Word],
COUNT(*) AS [Counts]
FROM
(SELECT * FROM words) a
CROSS APPLY
(SELECT
ItemNumber = ROW_NUMBER() OVER(ORDER BY l.N1),
Item = LTRIM(RTRIM(SUBSTRING(a.text_column, l.N1, l.L1)))
FROM
(SELECT
s.N1,
L1 = ISNULL(NULLIF(CHARINDEX(' ',a.text_column,s.N1),0)-s.N1,4000)
FROM
(SELECT 1
UNION ALL
SELECT t.N+1
FROM
(SELECT TOP (ISNULL(DATALENGTH(a.text_column)/2,0))
ROW_NUMBER() OVER (ORDER BY (SELECT NULL))
FROM E4) t(N)
WHERE SUBSTRING(a.text_column ,t.N,1) = ' '
) s(N1)
) l(N1, L1)
) x
WHERE
x.item <> ''
AND x.Item NOT IN ('0o', '0s', '3a', '3b', '3d', '6b', '6o', 'a', 'a1', 'a2', 'a3', 'a4', 'ab', 'able', 'about', 'above', 'abst', 'ac', 'accordance', 'according', 'accordingly', 'across', 'act', 'actually', 'ad', 'added', 'adj', 'ae', 'af', 'affected', 'affecting', 'affects', 'after', 'afterwards', 'ag', 'again', 'against', 'ah', 'ain', 'ain''t', 'aj', 'al', 'all', 'allow', 'allows', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst', 'amount', 'an', 'and', 'announce', 'another', 'any', 'anybody', 'anyhow', 'anymore', 'anyone', 'anything', 'anyway', 'anyways', 'anywhere', 'ao', 'ap', 'apart', 'apparently', 'appear', 'appreciate', 'appropriate', 'approximately', 'ar', 'are', 'aren', 'arent', 'aren''t', 'arise', 'around', 'as', 'a''s', 'aside', 'ask', 'asking', 'associated', 'at', 'au', 'auth', 'av', 'available', 'aw', 'away', 'awfully', 'ax', 'ay', 'az', 'b', 'b1', 'b2', 'b3', 'ba', 'back', 'bc', 'bd', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'begin', 'beginning', 'beginnings', 'begins', 'behind', 'being', 'believe', 'below', 'beside', 'besides', 'best', 'better', 'between', 'beyond', 'bi', 'bill', 'biol', 'bj', 'bk', 'bl', 'bn', 'both', 'bottom', 'bp', 'br', 'brief', 'briefly', 'bs', 'bt', 'bu', 'but', 'bx', 'by', 'c', 'c1', 'c2', 'c3', 'ca', 'call', 'came', 'can', 'cannot', 'cant', 'can''t', 'cause', 'causes', 'cc', 'cd', 'ce', 'certain', 'certainly', 'cf', 'cg', 'ch', 'changes', 'ci', 'cit', 'cj', 'cl', 'clearly', 'cm', 'c''mon', 'cn', 'co', 'com', 'come', 'comes', 'con', 'concerning', 'consequently', 'consider', 'considering', 'contain', 'containing', 'contains', 'corresponding', 'could', 'couldn', 'couldnt', 'couldn''t', 'course', 'cp', 'cq', 'cr', 'cry', 'cs', 'c''s', 'ct', 'cu', 'currently', 'cv', 'cx', 'cy', 'cz', 'd', 'd2', 'da', 'date', 'dc', 'dd', 'de', 'definitely', 'describe', 'described', 'despite', 'detail', 'df', 'di', 'did', 'didn', 'didn''t', 'different', 'dj', 'dk', 'dl', 'do', 'does', 'doesn', 'doesn''t', 'doing', 'don', 'done', 'don''t', 'down', 'downwards', 'dp', 'dr', 'ds', 'dt', 'du', 'due', 'during', 'dx', 'dy', 'e', 'e2', 'e3', 'ea', 'each', 'ec', 'ed', 'edu', 'ee', 'ef', 'effect', 'eg', 'ei', 'eight', 'eighty', 'either', 'ej', 'el', 'eleven', 'else', 'elsewhere', 'em', 'empty', 'en', 'end', 'ending', 'enough', 'entirely', 'eo', 'ep', 'eq', 'er', 'es', 'especially', 'est', 'et', 'et-al', 'etc', 'eu', 'ev', 'even', 'ever', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'ex', 'exactly', 'example', 'except', 'ey', 'f', 'f2', 'fa', 'far', 'fc', 'few', 'ff', 'fi', 'fifteen', 'fifth', 'fify', 'fill', 'find', 'fire', 'first', 'five', 'fix', 'fj', 'fl', 'fn', 'fo', 'followed', 'following', 'follows', 'for', 'former', 'formerly', 'forth', 'forty', 'found', 'four', 'fr', 'from', 'front', 'fs', 'ft', 'fu', 'full', 'further', 'furthermore', 'fy', 'g', 'ga', 'gave', 'ge', 'get', 'gets', 'getting', 'gi', 'give', 'given', 'gives', 'giving', 'gj', 'gl', 'go', 'goes', 'going', 'gone', 'got', 'gotten', 'gr', 'greetings', 'gs', 'gy', 'h', 'h2', 'h3', 'had', 'hadn', 'hadn''t', 'happens', 'hardly', 'has', 'hasn', 'hasnt', 'hasn''t', 'have', 'haven', 'haven''t', 'having', 'he', 'hed', 'he''d', 'he''ll', 'hello', 'help', 'hence', 'her', 'here', 'hereafter', 'hereby', 'herein', 'heres', 'here''s', 'hereupon', 'hers', 'herself', 'hes', 'he''s', 'hh', 'hi', 'hid', 'him', 'himself', 'his', 'hither', 'hj', 'ho', 'home', 'hopefully', 'how', 'howbeit', 'however', 'how''s', 'hr', 'hs', 'http', 'hu', 'hundred', 'hy', 'i', 'i2', 'i3', 'i4', 'i6', 'i7', 'i8', 'ia', 'ib', 'ibid', 'ic', 'id', 'i''d', 'ie', 'if', 'ig', 'ignored', 'ih', 'ii', 'ij', 'il', 'i''ll', 'im', 'i''m', 'immediate', 'immediately', 'importance', 'important', 'in', 'inasmuch', 'inc', 'indeed', 'index', 'indicate', 'indicated', 'indicates', 'information', 'inner', 'insofar', 'instead', 'interest', 'into', 'invention', 'inward', 'io', 'ip', 'iq', 'ir', 'is', 'isn', 'isn''t', 'it', 'itd', 'it''d', 'it''ll', 'its', 'it''s', 'itself', 'iv', 'i''ve', 'ix', 'iy', 'iz', 'j', 'jj', 'jr', 'js', 'jt', 'ju', 'just', 'k', 'ke', 'keep', 'keeps', 'kept', 'kg', 'kj', 'km', 'know', 'known', 'knows', 'ko', 'l', 'l2', 'la', 'largely', 'last', 'lately', 'later', 'latter', 'latterly', 'lb', 'lc', 'le', 'least', 'les', 'less', 'lest', 'let', 'lets', 'let''s', 'lf', 'like', 'liked', 'likely', 'line', 'little', 'lj', 'll', 'll', 'ln', 'lo', 'look', 'looking', 'looks', 'los', 'lr', 'ls', 'lt', 'ltd', 'm', 'm2', 'ma', 'made', 'mainly', 'make', 'makes', 'many', 'may', 'maybe', 'me', 'mean', 'means', 'meantime', 'meanwhile', 'merely', 'mg', 'might', 'mightn', 'mightn''t', 'mill', 'million', 'mine', 'miss', 'ml', 'mn', 'mo', 'more', 'moreover', 'most', 'mostly', 'move', 'mr', 'mrs', 'ms', 'mt', 'mu', 'much', 'mug', 'must', 'mustn', 'mustn''t', 'my', 'myself', 'n', 'n2', 'na', 'name', 'namely', 'nay', 'nc', 'nd', 'ne', 'near', 'nearly', 'necessarily', 'necessary', 'need', 'needn', 'needn''t', 'needs', 'neither', 'never', 'nevertheless', 'new', 'next', 'ng', 'ni', 'nine', 'ninety', 'nj', 'nl', 'nn', 'no', 'nobody', 'non', 'none', 'nonetheless', 'noone', 'nor', 'normally', 'nos', 'not', 'noted', 'nothing', 'novel', 'now', 'nowhere', 'nr', 'ns', 'nt', 'ny', 'o', 'oa', 'ob', 'obtain', 'obtained', 'obviously', 'oc', 'od', 'of', 'off', 'often', 'og', 'oh', 'oi', 'oj', 'ok', 'okay', 'ol', 'old', 'om', 'omitted', 'on', 'once', 'one', 'ones', 'only', 'onto', 'oo', 'op', 'oq', 'or', 'ord', 'os', 'ot', 'other', 'others', 'otherwise', 'ou', 'ought', 'our', 'ours', 'ourselves', 'out', 'outside', 'over', 'overall', 'ow', 'owing', 'own', 'ox', 'oz', 'p', 'p1', 'p2', 'p3', 'page', 'pagecount', 'pages', 'par', 'part', 'particular', 'particularly', 'pas', 'past', 'pc', 'pd', 'pe', 'per', 'perhaps', 'pf', 'ph', 'pi', 'pj', 'pk', 'pl', 'placed', 'please', 'plus', 'pm', 'pn', 'po', 'poorly', 'possible', 'possibly', 'potentially', 'pp', 'pq', 'pr', 'predominantly', 'present', 'presumably', 'previously', 'primarily', 'probably', 'promptly', 'proud', 'provides', 'ps', 'pt', 'pu', 'put', 'py', 'q', 'qj', 'qu', 'que', 'quickly', 'quite', 'qv', 'r', 'r2', 'ra', 'ran', 'rather', 'rc', 'rd', 're', 'readily', 'really', 'reasonably', 'recent', 'recently', 'ref', 'refs', 'regarding', 'regardless', 'regards', 'related', 'relatively', 'research', 'research-articl', 'respectively', 'resulted', 'resulting', 'results', 'rf', 'rh', 'ri', 'right', 'rj', 'rl', 'rm', 'rn', 'ro', 'rq', 'rr', 'rs', 'rt', 'ru', 'run', 'rv', 'ry', 's', 's2', 'sa', 'said', 'same', 'saw', 'say', 'saying', 'says', 'sc', 'sd', 'se', 'sec', 'second', 'secondly', 'section', 'see', 'seeing', 'seem', 'seemed', 'seeming', 'seems', 'seen', 'self', 'selves', 'sensible', 'sent', 'serious', 'seriously', 'seven', 'several', 'sf', 'shall', 'shan', 'shan''t', 'she', 'shed', 'she''d', 'she''ll', 'shes', 'she''s', 'should', 'shouldn', 'shouldn''t', 'should''ve', 'show', 'showed', 'shown', 'showns', 'shows', 'si', 'side', 'significant', 'significantly', 'similar', 'similarly', 'since', 'sincere', 'six', 'sixty', 'sj', 'sl', 'slightly', 'sm', 'sn', 'so', 'some', 'somebody', 'somehow', 'someone', 'somethan', 'something', 'sometime', 'sometimes', 'somewhat', 'somewhere', 'soon', 'sorry', 'sp', 'specifically', 'specified', 'specify', 'specifying', 'sq', 'sr', 'ss', 'st', 'still', 'stop', 'strongly', 'sub', 'substantially', 'successfully', 'such', 'sufficiently', 'suggest', 'sup', 'sure', 'sy', 'system', 'sz', 't', 't1', 't2', 't3', 'take', 'taken', 'taking', 'tb', 'tc', 'td', 'te', 'tell', 'ten', 'tends', 'tf', 'th', 'than', 'thank', 'thanks', 'thanx', 'that', 'that''ll', 'thats', 'that''s', 'that''ve', 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'thence', 'there', 'thereafter', 'thereby', 'thered', 'therefore', 'therein', 'there''ll', 'thereof', 'therere', 'theres', 'there''s', 'thereto', 'thereupon', 'there''ve', 'these', 'they', 'theyd', 'they''d', 'they''ll', 'theyre', 'they''re', 'they''ve', 'thickv', 'thin', 'think', 'third', 'this', 'thorough', 'thoroughly', 'those', 'thou', 'though', 'thoughh', 'thousand', 'three', 'throug', 'through', 'throughout', 'thru', 'thus', 'ti', 'til', 'tip', 'tj', 'tl', 'tm', 'tn', 'to', 'together', 'too', 'took', 'top', 'toward', 'towards', 'tp', 'tq', 'tr', 'tried', 'tries', 'truly', 'try', 'trying', 'ts', 't''s', 'tt', 'tv', 'twelve', 'twenty', 'twice', 'two', 'tx', 'u', 'u201d', 'ue', 'ui', 'uj', 'uk', 'um', 'un', 'under', 'unfortunately', 'unless', 'unlike', 'unlikely', 'until', 'unto', 'uo', 'up', 'upon', 'ups', 'ur', 'us', 'use', 'used', 'useful', 'usefully', 'usefulness', 'uses', 'using', 'usually', 'ut', 'v', 'va', 'value', 'various', 'vd', 've', 've', 'very', 'via', 'viz', 'vj', 'vo', 'vol', 'vols', 'volumtype', 'vq', 'vs', 'vt', 'vu', 'w', 'wa', 'want', 'wants', 'was', 'wasn', 'wasnt', 'wasn''t', 'way', 'we', 'wed', 'we''d', 'welcome', 'well', 'we''ll', 'well-b', 'went', 'were', 'we''re', 'weren', 'werent', 'weren''t', 'we''ve', 'what', 'whatever', 'what''ll', 'whats', 'what''s', 'when', 'whence', 'whenever', 'when''s', 'where', 'whereafter', 'whereas', 'whereby', 'wherein', 'wheres', 'where''s', 'whereupon', 'wherever', 'whether', 'which', 'while', 'whim', 'whither', 'who', 'whod', 'whoever', 'whole', 'who''ll', 'whom', 'whomever', 'whos', 'who''s', 'whose', 'why', 'why''s', 'wi', 'widely', 'will', 'willing', 'wish', 'with', 'within', 'without', 'wo', 'won', 'wonder', 'wont', 'won''t', 'words', 'world', 'would', 'wouldn', 'wouldnt', 'wouldn''t', 'www', 'x', 'x1', 'x2', 'x3', 'xf', 'xi', 'xj', 'xk', 'xl', 'xn', 'xo', 'xs', 'xt', 'xv', 'xx', 'y', 'y2', 'yes', 'yet', 'yj', 'yl', 'you', 'youd', 'you''d', 'you''ll', 'your', 'youre', 'you''re', 'yours', 'yourself', 'yourselves', 'you''ve', 'yr', 'ys', 'yt', 'z', 'zero', 'zi', 'zz')
GROUP BY x.Item
ORDER BY COUNT(*) DESC
Here's the result of the above code, as you can see it's not counting correctly:
Word Counts
server 2
sql 2
data 1
database 1
popular 1
powerful 1
Can anyone help on this? Would be really appreciated!

You can make use of String_split here, such as
select value Word, Count(*) Counts
from words
cross apply String_Split(text_column, ' ')
where value not in(exclude list)
group by value
order by counts desc;

You should should the string_split function -- like this
SELECT id, value as aword
FROM words
CROSS APPLY STRING_SPLIT(text_column, ',');
This will create a table with all the words by id -- to get the count do this:
SELECT aword, count(*) as counts
FROM (
SELECT id, value as aword
FROM words
CROSS APPLY STRING_SPLIT(text_column, ',');
) x
GROUP BY aword
You may need to lower case the LOWER(text_column) if you want it to not matter

If you don't have access to STRING_SPLIT function, you can use weird xml trick to convert space to a word node and then shred it with nodes function:
select word, COUNT(*)
from (
select n.value('.', 'nvarchar(50)') AS word
from (
VALUES
(1, 'SQL Server is a popular database management system'),
(2, 'It is widely used for data storage and retrieval'),
(3, 'SQL Server is a powerful tool for data analysis')
) AS t (id, txt)
CROSS APPLY (
SELECT CAST('<x>' + REPLACE(txt, ' ', '</x><x>') + '</x>' AS XML) x
) x
CROSS APPLY x.nodes('x') z(n)
) w
GROUP BY word
Of course, this will fail on "bad" words and invalid xml-characters but it can be worked on. Text processing has never been SQL Server's strong-point though, so probably better to use some NLP library to do this kind of stuff

Population by United States Region

Using SQL Let’s say we have a dataset of population by state (e.g., Vermont, 623,251, and so on), but we want to know the population by United States region (e.g., Midwest, 68,985,454). Could you describe how you would go about doing that?
Dataset from census.gov
Where I'm stuck at
--First I created a table with a state and population column.
CREATE TABLE states (
state VARCHAR(20),
population INT
);
--Then I uploaded a CSV file from census.gov that I cleaned up.
SELECT * FROM states;
--Created a temporary table to add in the region column.
DROP TABLE IF EXISTS temp_Regions;
CREATE TEMP TABLE temp_Regions (
state VARCHAR(20),
state_pop INT,
region VARCHAR(20),
region_pop INT
);
INSERT INTO temp_Regions
SELECT state, population
FROM states;
--Used CASE WHEN statements to put states in to their respective regions.
SELECT state,
CASE WHEN state IN ('Connecticut', 'Maine', 'Massachusetts', 'New Hampshire', 'Rhode Island', 'Vermont', 'New Jersey', 'New York', 'Pennsylvania') THEN 'Northeast'
WHEN state IN ('Illinois', 'Indiana', 'Michigan', 'Ohio', 'Wisconsin', 'Iowa', 'Kansas', 'Minnesota', 'Missouri', 'Nebraska', 'North Dakota', 'South Dakota') THEN 'Midwest'
WHEN state IN ('Delaware', 'District of Columbia', 'Florida', 'Georgia', 'Maryland', 'North Carolina', 'South Carolina', 'Virginia', 'West Virginia', 'Alabama', 'Kentucky', 'Mississippi', 'Tennessee', 'Arkansas', 'Louisiana', 'Oklahoma', 'Texas') THEN 'South'
WHEN state IN ('Arizona', 'Colorado', 'Idaho', 'Montana', 'Nevada', 'New Mexico', 'Utah', 'Wyoming', 'Alaska', 'California', 'Hawaii', 'Oregon', 'Washington') THEN 'West'
END AS region, state_pop, region_pop
FROM temp_Regions;
--Now I'm stuck at this point. I'm unable to get data into the region_pop column. How do I get the sum of the populations by U.S. Region?
Let me know if you need further clarification on things. Thanks for your help y'all!

You can make use of analytical function sum() over(partition by) to achieve this
with data
as (
SELECT state
,CASE WHEN state IN ('Connecticut', 'Maine', 'Massachusetts', 'New Hampshire', 'Rhode Island', 'Vermont', 'New Jersey', 'New York', 'Pennsylvania') THEN 'Northeast'
WHEN state IN ('Illinois', 'Indiana', 'Michigan', 'Ohio', 'Wisconsin', 'Iowa', 'Kansas', 'Minnesota', 'Missouri', 'Nebraska', 'North Dakota', 'South Dakota') THEN 'Midwest'
WHEN state IN ('Delaware', 'District of Columbia', 'Florida', 'Georgia', 'Maryland', 'North Carolina', 'South Carolina', 'Virginia', 'West Virginia', 'Alabama', 'Kentucky', 'Mississippi', 'Tennessee', 'Arkansas', 'Louisiana', 'Oklahoma', 'Texas') THEN 'South'
WHEN state IN ('Arizona', 'Colorado', 'Idaho', 'Montana', 'Nevada', 'New Mexico', 'Utah', 'Wyoming', 'Alaska', 'California', 'Hawaii', 'Oregon', 'Washington') THEN 'West'
END AS region
, state_pop
FROM temp_Regions
)
select state
,region
,state_pop
,sum(state_pop) over(partition by region) as region_population
from data

What is the function to divide 2 values from the same column with conditions

I would like to return my total return divided by my total sale which are under the same column
select
(select count(order_type_id) from ods_emea_all.order_emea
where order_type_id in ('Return', 'RETURN')
and brand_cd =('PB')
and iso_country_cd IN ('IT', 'ES', 'GB', 'FR', 'DE'))*100/
(select count(order_type_id )
from ods_emea_all.order_emea
where order_type_id in ('Sale','SALE')
and brand_cd =('PB')
and iso_country_cd IN ('IT', 'ES', 'GB', 'FR', 'DE'))
AS brand_return
from ods_emea_all.order_emea

If you have the following table with contents:
CREATE TABLE order_emea (
order_type_id VARCHAR(100),
brand_cd VARCHAR(100),
iso_country_cd VARCHAR(100)
);
INSERT INTO order_emea VALUES
('Sale', 'PB', 'IT'),
('SALE', 'PB', 'FR'),
('Sale', 'PB', 'IT'),
('sale', 'PB', 'ES'),
('SALe', 'PB', 'ES'),
('sAle', 'PB', 'GB'),
('saLe', 'PB', 'FR'),
('sale', 'PB', 'DE'),
('Sale', 'PB', 'DE'),
('sale', 'PB', 'FR'),
('Return', 'PB', 'FR'),
('RETURN', 'PB', 'FR'),
('return', 'PB', 'GB'),
('REturn', 'PB', 'IT'),
('rEturn', 'PB', 'IT');
The following query will get you what you want:
WITH base_table_n_returns_to_n_sales AS (
SELECT
SUM(CASE WHEN
LOWER(order_type_id) = 'return'
AND LOWER(brand_cd) = 'pb'
AND LOWER(iso_country_cd) IN ('IT', 'ES', 'GB', 'FR', 'DE')
THEN 1 ELSE 0 END) AS n_returns,
SUM(CASE WHEN
LOWER(order_type_id) = 'sale'
AND LOWER(brand_cd) = 'pb'
AND LOWER(iso_country_cd) IN ('IT', 'ES', 'GB', 'FR', 'DE')
THEN 1 ELSE 0 END) AS n_sales
FROM order_emea
)
SELECT
*,
n_returns / CAST(NULLIF(n_sales, 0) AS FLOAT) AS ratio_n_returns_to_n_sales
FROM base_table_n_returns_to_n_sales
See this fiddle.

SQL Select Convert State Name To Abbreviation

In a SQL select statement, how to convert a full state name to state abbreviation (e.g. New York to NY)? I'd like to do this without joins if possible. What would the regexp_replace look like?
select regexp_replace(table.state, 'New York', 'NY', 'g') as state
Can this approach be done en mass for all states?
For reference list of states names and abbreviations: https://gist.github.com/esfand/9443427.

Here it is in raw WHEN/THEN form if needed, along with Canadian provinces:
CASE "YOUR COLUMN CONTAINING FULL STATE NAMES"
WHEN 'Alabama' THEN 'AL'
WHEN 'Alaska' THEN 'AK'
WHEN 'Arizona' THEN 'AZ'
WHEN 'Arkansas' THEN 'AR'
WHEN 'California' THEN 'CA'
WHEN 'Colorado' THEN 'CO'
WHEN 'Connecticut' THEN 'CT'
WHEN 'Delaware' THEN 'DE'
WHEN 'District of Columbia' THEN 'DC'
WHEN 'Florida' THEN 'FL'
WHEN 'Georgia' THEN 'GA'
WHEN 'Hawaii' THEN 'HI'
WHEN 'Idaho' THEN 'ID'
WHEN 'Illinois' THEN 'IL'
WHEN 'Indiana' THEN 'IN'
WHEN 'Iowa' THEN 'IA'
WHEN 'Kansas' THEN 'KS'
WHEN 'Kentucky' THEN 'KY'
WHEN 'Louisiana' THEN 'LA'
WHEN 'Maine' THEN 'ME'
WHEN 'Maryland' THEN 'MD'
WHEN 'Massachusetts' THEN 'MA'
WHEN 'Michigan' THEN 'MI'
WHEN 'Minnesota' THEN 'MN'
WHEN 'Mississippi' THEN 'MS'
WHEN 'Missouri' THEN 'MO'
WHEN 'Montana' THEN 'MT'
WHEN 'Nebraska' THEN 'NE'
WHEN 'Nevada' THEN 'NV'
WHEN 'New Hampshire' THEN 'NH'
WHEN 'New Jersey' THEN 'NJ'
WHEN 'New Mexico' THEN 'NM'
WHEN 'New York' THEN 'NY'
WHEN 'North Carolina' THEN 'NC'
WHEN 'North Dakota' THEN 'ND'
WHEN 'Ohio' THEN 'OH'
WHEN 'Oklahoma' THEN 'OK'
WHEN 'Oregon' THEN 'OR'
WHEN 'Pennsylvania' THEN 'PA'
WHEN 'Rhode Island' THEN 'RI'
WHEN 'South Carolina' THEN 'SC'
WHEN 'South Dakota' THEN 'SD'
WHEN 'Tennessee' THEN 'TN'
WHEN 'Texas' THEN 'TX'
WHEN 'Utah' THEN 'UT'
WHEN 'Vermont' THEN 'VT'
WHEN 'Virginia' THEN 'VA'
WHEN 'Washington' THEN 'WA'
WHEN 'West Virginia' THEN 'WV'
WHEN 'Wisconsin' THEN 'WI'
WHEN 'Wyoming' THEN 'WY'
WHEN 'Alberta' THEN 'AB'
WHEN 'British Columbia' THEN 'BC'
WHEN 'Manitoba' THEN 'MB'
WHEN 'New Brunswick' THEN 'NB'
WHEN 'Newfoundland and Labrador' THEN 'NL'
WHEN 'Northwest Territories' THEN 'NT'
WHEN 'Nova Scotia' THEN 'NS'
WHEN 'Nunavut' THEN 'NU'
WHEN 'Ontario' THEN 'ON'
WHEN 'Prince Edward Island' THEN 'PE'
WHEN 'Quebec' THEN 'QC'
WHEN 'Saskatchewan' THEN 'SK'
WHEN 'Yukon Territory' THEN 'YT'
ELSE NULL
END

With PostgreSQL you can use JSON
select '{"Alabama": "AL", "Alaska": "AK"}'::json->'Alabama'
You can also use a column reference instead of a string literal
select
'{"Alabama": "AL", "Alaska": "AK"}'::json->example.state
from
(values ('Alabama')) example(state)

As comments suggested that a join is needed. Below is what I ended up doing. Let me know if there is a better way.
with states(name, abbr) as (
select
*
from
(values ('Alabama', 'AL'),
('Alaska', 'AK'),
('Arizona', 'AZ'),
('Arkansas', 'AR'),
('California', 'CA'),
('Colorado', 'CO'),
('Connecticut', 'CT'),
('Delaware', 'DE'),
('District of Columbia', 'DC'),
('Florida', 'FL'),
('Georgia', 'GA'),
('Hawaii', 'HI'),
('Idaho', 'ID'),
('Illinois', 'IL'),
('Indiana', 'IN'),
('Iowa', 'IA'),
('Kansas', 'KS'),
('Kentucky', 'KY'),
('Louisiana', 'LA'),
('Maine', 'ME'),
('Maryland', 'MD'),
('Massachusetts', 'MA'),
('Michigan', 'MI'),
('Minnesota', 'MN'),
('Mississippi', 'MS'),
('Missouri', 'MO'),
('Montana', 'MT'),
('Nebraska', 'NE'),
('Nevada', 'NV'),
('New Hampshire', 'NH'),
('New Jersey', 'NJ'),
('New Mexico', 'NM'),
('New York', 'NY'),
('North Carolina', 'NC'),
('North Dakota', 'ND'),
('Ohio', 'OH'),
('Oklahoma', 'OK'),
('Oregon', 'OR'),
('Pennsylvania', 'PA'),
('Rhode Island', 'RI'),
('South Carolina', 'SC'),
('South Dakota', 'SD'),
('Tennessee', 'TN'),
('Texas', 'TX'),
('Utah', 'UT'),
('Vermont', 'VT'),
('Virginia', 'VA'),
('Washington', 'WA'),
('West Virginia', 'WV'),
('Wisconsin', 'WI'),
('Wyoming', 'WY')) as state
)
And select (select states.abbr from states where name = state_name)

How do I use count and group by as a condition for another table

So to preface, I'm a first-year comp sci student and we've only just started on SQL, so forgive me if the solution seems obvious.
We were given a database for Zoo, which has tables for Animals, Keepers, and a link entity (if that's the right word) for care roles, connecting the two.
(Schema below)
CREATE TABLE Animal (ID VARCHAR(6) PRIMARY KEY, Name VARCHAR(10), Species
VARCHAR(20),
Age SMALLINT, Sex VARCHAR(1), Weight SMALLINT, F_ID VARCHAR(6), M_ID
VARCHAR(6));
CREATE TABLE Keeper (Staff_ID VARCHAR(6) PRIMARY KEY, Keeper_Name
VARCHAR(20), Specialisation VARCHAR(20));
CREATE TABLE Care_Role (ID VARCHAR(6), Staff_ID VARCHAR(6), Role
VARCHAR(10), PRIMARY KEY (ID, Staff_ID));
Now the task we've been given is to work out which Keepers have been caring for more than 10 animals of the same species using the following data:
INSERT INTO Animal VALUES
('11', 'Horace', 'Marmoset', 99, 'M', 5, '2','1'),
('12', 'sghgdht', 'Marmoset', 42, 'M', 3, '2','1'),
('13', 'xgnyn', 'Marmoset', 37, 'F', 3, '1','11'),
('14', 'sbfdfbng', 'Marmoset', 12, 'F', 3, '1','11'),
('15', 'fdghd', 'Marmoset', 12, 'M', 3, '1','11'),
('16', 'Fred', 'Marmoset', 6, 'M', 3, '15','1'),
('17', 'Mary', 'Marmoset', 3, 'F', 3, '8','14'),
('18', 'Jane', 'Marmoset', 5, 'F', 3, '7','13'),
('19', 'dfgjtjt', 'Marmoset', 5, 'M', 3, '16','17'),
('20', 'Eric', 'Marmoset', 5, 'M', 3, '12','13'),
('21', 'tukyufyu', 'Marmoset', 5, 'M', 3, '12','73'),
('31', 'hgndghmd', 'Giraffe', 99, 'M', 5, '201','1'),
('32', 'sghgdht', 'Giraffe', 42, 'M', 3, '201','1'),
('33', 'xgnyn', 'Giraffe', 37, 'F', 3, '111','1'),
('34', 'sbfdfbng', 'Giraffe', 12, 'F', 3, '111','1'),
('35', 'fdghd', 'Giraffe', 12, 'M', 3, '111','6'),
('36', 'Fred', 'Lion', 6, 'M', 3, '151','111'),
('37', 'Mary', 'Lion', 3, 'F', 3, '81','114'),
('38', 'Jane', 'Lion', 5, 'F', 3, '71','113'),
('39', 'Kingsly', 'Lion', 9, 'M', 3, '161','117'),
('40', 'Eric', 'Lion', 11, 'M', 3, '121','113'),
('41', 'tukyufyu', 'Lion', 2, 'M', 3, '121','173'),
('61', 'hgndghmd', 'Elephant', 6, 'F', 225, '201','111'),
('62', 'sghgdht', 'Elephant', 10, 'F', 230, '201','111'),
('63', 'xgnyn', 'Elephant', 5, 'F', 300, '111','121'),
('64', 'sbfdfbng', 'Elephant', 11, 'F', 173, '111','121'),
('65', 'fdghd', 'Elephant', 12, 'F', 231, '111','666'),
('66', 'Fred', 'Elephant', 17, 'F', 333, '151','147'),
('67', 'Mary', 'Elephant', 3, 'F', 272, '81','148'),
('68', 'Jane', 'Elephant', 8, 'F', 47, '71','136'),
('69', 'dfgjtjt', 'Elephant', 9, 'F', 131, '161','172'),
('70', 'Eric', 'Elephant', 10, 'F', 333, '121','136'),
('71', 'tukyufyu', 'Elephant', 7, 'M', 114, '121','731');
INSERT INTO Keeper VALUES
('1', 'Roger', 'tdfhuihiu'),
('2', 'Sidra', 'rgegegtnrty'),
('3', 'Amit', 'ergetetnt'),
('4', 'Lucia', 'dvojivhwivih');
INSERT INTO Care_Role VALUES
('32', '1', 'feeding'),
('32', '2', 'washing'),
('61', '1', 'feeding'),
('62', '1', 'feeding'),
('63', '1', 'feeding'),
('64', '1', 'feeding'),
('65', '1', 'feeding'),
('66', '1', 'feeding'),
('67', '1', 'feeding'),
('68', '1', 'feeding'),
('69', '1', 'feeding'),
('70', '1', 'feeding'),
('71', '1', 'feeding'),
('11', '4', 'feeding'),
('12', '4', 'feeding'),
('13', '4', 'feeding'),
('14', '4', 'feeding'),
('15', '4', 'feeding'),
('16', '4', 'feeding'),
('17', '4', 'feeding'),
('18', '4', 'feeding'),
('19', '4', 'feeding'),
('20', '4', 'feeding'),
('21', '4', 'feeding');
So far what I've managed to come up with is this:
SELECT Keeper.Keeper_Name, Animal.Species, COUNT(Animal.Species)
FROM Keeper
JOIN Care_Role
ON Keeper.Staff_ID = Care_Role.Staff_ID
JOIN Animal
ON Care_Role.ID = Animal.ID
GROUP BY Animal.Species
But this is returning more than just the name (which is what I want), as well as showing all the people who have looked after animals, rather than just those who have looked after 10 or more, I was wondering if anyone had any ideas on how to help with this? Many thanks!

Your query should be returning an error, because Keeper.Keeper_name is not in the GROUP BY. You have made a good attempt. A reasonable way to start the query is:
SELECT k.Keeper_Name, a.Species, COUNT(*)
FROM Keeper k JOIN
Care_Role cr
ON k.Staff_ID = cr.Staff_ID JOIN
Animal a
ON cr.ID = a.ID
GROUP BY k.Keeper_Name, a.Species;
This will return the number of animals of a given species that each keeper cares for.
Note the following:
Table aliases are abbreviations for the table.
All column names are qualified.
This uses the shorthand of COUNT(*) instead of counting some particular column.
Your question adds an additional condition about 10 animals. You can fit that in using a HAVING clause.