I have 2 tables (MS ACCESS):
Table "Orders"
OrderID Product Product_Group Client Client_Group Revenue
1 Cars Vehicles Men People 10 000
2 Houses NC_Assets Women People 15 000
3 Houses NC_Assets Partnersh Companies 12 000
4 Cars Vehicles Corps Companies 3 000
Table "Gouping"
Product Product_Group Client Client_Group Tax rate
Cars Companies Taxable 30%
Vehicles Companies Taxable 15%
Houses People Taxable 13%
Houses Women Taxable 15%
I want to join these tables to see which orders will fall into which taxable group. As you can see some products/clients are mapped differently than their groups -> if that is the case, the query should return only one record for this pair and exclude any pairing containing their groups. In pseudo-code:
If there's product-client grouping, return this record Else
If there's product-client grouping ---//----- else
If there's product group - client ----///-----else
If there's product group-client group ---///----
End if * 4
In that order.
Now my query (pseudo):
SELECT [Orders].*, [Grouping].* FROM [Orders] LEFT JOIN [Grouping] ON
(([Orders].Product = [Grouping].Product OR [Orders].Product_Group = [Grouping].Product_Group) AND
([Orders].Client = [Grouping].Client OR [Orders].Client_Group = [Grouping].Client_Group))
Returns both Cars-Companies and Vehicles-Companies. I'm out of ideas how to set it up to get only the most granular records from each combination. UNION? NOT EXISTS?
Any help appreciated.
I want to join these tables to see how many orders qualify as good,
mediocre etc.
Sounds like you want counts of the particular conditions...Assuming you have a SUM and CASE (I haven't written queries for MS Access in about 10 years...), here's some pseudo-code that should get you started:
SELECT SUM(CASE WHEN {mediocre-conditions} THEN 1 ELSE 0 END) AS MediocreCount,
SUM(CASE WHEN {good-conditions} THEN 1 ELSE 0 END) AS GoodCount,
SUM(CASE WHEN {great-conditions} THEN 1 ELSE 0 END) AS GreatCount
FROM [Orders] LEFT JOIN [Grouping] ON (([Orders].Product = [Grouping].Product OR [Orders].Product_Group = [Grouping].Product_Group) AND ([Orders].Client = [Grouping].Client OR [Orders].Client_Group = [Grouping].Client_Group))
[update] I don't like giving bad answers, so did a quick look...based on this link: Does MS Access support "CASE WHEN" clause if connect with ODBC?, it appears you may be able to do:
SELECT SUM(IIF({mediocre-conditions},1,0)) AS MediocreCount,
SUM(IIF({good-conditions},1,0)) AS GoodCount,
SUM(IIF({great-conditions},1,0)) AS GreatCount
Related
I want to use a simple private cashflow database. Nothing special.
Therefor I use a table "finance_flow", where I can put all my in- and outcomes.
Income -> amount > 0
outcome -> amount < 0
Table structure
table "finance_flow" with example-data
id
category_id
amount
date
note
int
int
float
timestamp
varchar
1
1
+60,00
5.2.23
use for xy
2
2
-10,00
8.2.23
to Tom for school
3
3
-8,96
8.2.23
milk, bread, cheese
table "category"
id
name
1
tips
2
kids
3
shop
Of course there is a correct foreign-key-constraint.
What I want:
I want some statistical data, for example:
-current status of my money
-total outcomes for each category
-procentual values of those would be nice
I know how to get the total current state:
SELECT sum(amount) as total FROM finance_flow
I know how to get the total per category
SELECT abs(sum(amount)) as total_per_cat, category.name
FROM finance_flow
LEFT JOIN category ON financeflow.cat_id = category.id
GROUP BY category_id [WHERE date = 'february']
(Here I use the function abs(x), because I am not interessted in the sign.
The where-clause is optional, I want this, if the basics are correct, for monthly reports.
How to get the procentual values?
Can I get all this stuff with one query? :)
Expected result:
procentual_per_cat = total_per_cut / total_income(february) * 100
where
total_per_cut = abs(sum(amount)) for category x
total_income(february) = 60
resulting table:
name
total_per_cat
procentual_per_cat
kids
10
16.67 %
shop
8.96
14.93 %
Calculating the percentage is the same a dividing by the total amount:
SELECT
abs(sum(amount)) as total_per_cat,
abs(sum(amount))/(select sum(amount) from finance_flow where amount>0) *100 as percentage,
category.name
FROM finance_flow
LEFT JOIN category ON finance_flow.cat_id = category.id
WHERE amount<0
GROUP BY category.name
see: DBFIDDLE
This might be difficult to explain. But Im trying to write a redshift sql query where I have want the count of organizations that fall into different market buckets. There are 50 markets. For example company x can be only be found in 1 market and company y can be found in 3 markets. I want to preface that I have over 10,000 companies to fit into these buckets. So ideally it would be more like, hypothetically 500 companies are found in 3 markets or 7 companies are found in 50 markets.
The table would like
Market Bucket
Org Count
1 Markets
3
2 Markets
1
3 Markets
0
select count(distinct case when enterprise_account = true and (market_name then organization_id end) as "1 Market" from organization_facts
I was trying to formulate the query from above but I got confused on how to effectively formulate the query
Organization Facts
Market Name
Org ID
Org Name
New York
15683
Company x
Orlando
38478
Company y
Twin Cities
2738
Company z
Twin Cities
15683
Company x
Detroit
99
Company xy
You would need a sub-query that retrieves the number of markets per company, and an outer query that summarises into a count of markets.
Something like:
with markets as (
select
org_name,
count(distinct market_name) as market_count
from organization_facts
)
select
market_count,
count(*) as org_count
from markets
group by market_count
order by market_count
If I follow you correctly, you can do this with two levels of aggregation. Assuming that org_id represents a company in your dataset:
select cnt_markets, count(*) cnt_org_id
from (select count(*) cnt_markets from organization_facts group by org_id) t
group by cnt_markets
The subquery counts the number of markets per company. I assumed no duplicate (ord_id, market_name) tuples in the table ; if that's not the case, then you need count(distinct market_name) instead of count(*) in that spot.
Then, the outer query just counts how many times each market count occurs in the subquery, which yields the result that you want.
Note that I left apart the enterprise_account column ,that appears in your query but not in your data.
This question already has answers here:
Two SQL LEFT JOINS produce incorrect result
(3 answers)
Closed 12 months ago.
I am having some troubles with a count function. The problem is given by a left join that I am not sure I am doing correctly.
Variables are:
Customer_name (buyer)
Product_code (what the customer buys)
Store (where the customer buys)
The datasets are:
Customer_df (list of customers and product codes of their purchases)
Store1_df (list of product codes per week, for Store 1)
Store2_df (list of product codes per day, for Store 2)
Final output desired:
I would like to have a table with:
col1: Customer_name;
col2: Count of items purchased in store 1;
col3: Count of items purchased in store 2;
Filters: date range
My query looks like this:
SELECT
DISTINCT
C_customer_name,
C.product_code,
COUNT(S1.product_code) AS s1_sales,
COUNT(S2.product_code) AS s2_sales,
FROM customer_df C
LEFT JOIN store1_df S1 USING(product_code)
LEFT JOIN store2_df S2 USING(product_code)
GROUP BY
customer_name, product_code
HAVING
S1_sales > 0
OR S2_sales > 0
The output I expect is something like this:
Customer_name
Product_code
Store1_weekly_sales
Store2_weekly_sales
Luigi
120012
4
8
James
100022
6
10
But instead, I get:
Customer_name
Product_code
Store1_weekly_sales
Store2_weekly_sales
Luigi
120012
290
60
James
100022
290
60
It works when instead of COUNT(product_code) I do COUNT(DSITINCT product_code) but I would like to avoid that because I would like to be able to aggregate on different timespans (e.g. if I do count distinct and take into account more than 1 week of data I will not get the right numbers)
My hypothesis are:
I am joining the tables in the wrong way
There is a problem when joining two datasets with different time aggregations
What am I doing wrong?
The reason as Philipxy indicated is common. You are getting a Cartesian result from your data thus bloating your numbers. To simplify, lets consider just a single customer purchasing one item from two stores. The first store has 3 purchases, the second store has 5 purchases. Your total count is 3 * 5. This is because for each entry in the first is also joined by the same customer id in the second. So 1st purchase is joined to second store 1-5, then second purchase joined to second store 1-5 and you can see the bloat. So, by having each store pre-query the aggregates per customer will have AT MOST, one record per customer per store (and per product as per your desired outcome).
select
c.customer_name,
AllCustProducts.Product_Code,
coalesce( PQStore1.SalesEntries, 0 ) Store1SalesEntries,
coalesce( PQStore2.SalesEntries, 0 ) Store2SalesEntries
from
customer_df c
-- now, we need all possible UNIQUE instances of
-- a given customer and product to prevent duplicates
-- for subsequent queries of sales per customer and store
JOIN
( select distinct customerid, product_code
from store1_df
union
select distinct customerid, product_code
from store2_df ) AllCustProducts
on c.customerid = AllCustProducts.customerid
-- NOW, we can join to a pre-query of sales at store 1
-- by customer id and product code. You may also want to
-- get sum( SalesDollars ) if available, just add respectively
-- to each sub-query below.
LEFT JOIN
( select
s1.customerid,
s1.product_code,
count(*) as SalesEntries
from
store1_df s1
group by
s1.customerid,
s1.product_code ) PQStore1
on AllCustProducts.customerid = PQStore1.customerid
AND AllCustProducts.product_code = PQStore1.product_code
-- now, same pre-aggregation to store 2
LEFT JOIN
( select
s2.customerid,
s2.product_code,
count(*) as SalesEntries
from
store2_df s2
group by
s2.customerid,
s2.product_code ) PQStore2
on AllCustProducts.customerid = PQStore2.customerid
AND AllCustProducts.product_code = PQStore2.product_code
No need for a group by or having since all entries in their respective pre-aggregates will result in a maximum of 1 record per unique combination. Now, as for your needs to filter by date ranges. I would just add a WHERE clause within each of the AllCustProducts, PQStore1, and PQStore2.
I have an issue where I need to essentially categorically sum dollars based on criteria from different columns. For instance, there are multiple ways that a Client can be categorized and in the categories, the only one that matters is HIT. If a client has at least one line that contains HIT it should always be categorized as HIT even when that specific line isn't (example (line 9). As you can see in my data set, client A has lines that are both HIT and NONE but since Client A has at least one line that is HIT, all of the dollars should be categorized as HITS dollars. Since none of the other clients have HIT categories, all of their dollars would go into NOT.
CLIENT DOLLARS CATEGORY
A 12434 HIT
B 212 NONE
C 21 NONE
D 1231 NONE
B 784 NONE
A 43577 HIT
D 64 NONE
A 123 NONE
D 12 NONE
A 53 NONE
A 10 NONE
I'm trying to build this into a CASE ie.
SELECT CASE
WHEN category = 'HIT' THEN 'HITS'
WHEN category <> 'HIT' THEN 'NOT'
ELSE 'OTHER' END AS 'RESULT'
SUM(dollars) AS Dollars
FROM table 1
GROUP BY 'RESULT'
Obviously this won't pick up HIT Dollars for Client A when the category is NONE. Any help would be greatly appreciated.
Thanks!
You could join your table with a subquery that lists the hit clients:
select (case when (hits.client is null)
then 0
else 1
end) as hit,
sum(dollars) as Dollars
from t
left outer join ( select distinct client
from t
where category = 'HIT' ) hits
on t.client = hits.client
group by hit;
SQL fiddle: http://sqlfiddle.com/#!9/8d403e/7/0
You need more information. I would suggest window functions:
SELECT (CASE WHEN is_hit = 1 THEN 'HITS'
ELSE 'NOT'
END) as result,
SUM(dollars) AS Sum_Dollars
FROM (SELECT dollars,
MAX(CASE WHEN category = 'HIT' THEN 1 ELSE 0 END) OVER (PARTITION BY client) as is_hit
FROM t
)
GROUP BY is_hit
SQL Fiddle: http://sqlfiddle.com/#!4/40351/6
I have three tables: Achievements, Characters, and Character_Achievements table that store's the ID's of completed achievements and user id. I am looking to get each category, total amount of points possible and also the amount completed.
I am able to get each category and the amount of points possible but I am unsuccessful at retrieving the completed count as well.
I currently use this to get each category and the amount of points possible
SELECT achievements.category, SUM(points) AS Total
FROM achievements
GROUP BY achievements.category ORDER BY achievements._id asc
I get these results.
Category Total
Operations 50
Events 25
I can also get the amount of points completed
SELECT achievements.category, SUM(points) AS Completed
FROM achievements
LEFT JOIN character_achievements
ON character_achievements.achievements_id = achievements._id
LEFT JOIN character
ON character_achievements.character_id = character._id
WHERE character._id = '1'
which returns this but only the categories that are completed. How do I combine these two queries together.
Category Completed
Operations 50
Events 25
I've tried UNION but it does not return the results I need.
Here are my example tables
Achievements Table
Category Title Points
Operations Epic Enemies 25
Operations Explosive Conflict 25
Events Bounty Contract 25
Character_Achievements Table
Character Character_id Achievements_id
Operations 1 1
Events 1 3
The results I'm looking for would like this.
Results
Category Completed Total
Operations 25 50
Events 25 25
I am able
If I'm understanding your question correctly, you can use SUM with CASE:
SELECT a.category,
SUM(CASE WHEN ca.achievements_id is not null then points end) AS Completed,
SUM(points) Total
FROM achievements a
LEFT JOIN character_achievements ca
ON ca.achievements_id = a._id
GROUP BY a.category
ORDER BY a._id asc