Power Queries merge/add rows from a file for each column of another query - merge

I have a query in power BI as follows:
|---------------------|---------------------|---------------------|
| Test | Wup | Sigma |
|---------------------|---------------------|---------------------|
| A | 65465 | 4wr44 |
|---------------------|---------------------|---------------------|
| B | 56 | fd54 |
|---------------------|---------------------|---------------------|
| C | 654564 | d340 |
|---------------------|---------------------|---------------------|
And another query as follows:
|---------------------|---------------------|---------------------|
| Test | Wup | Sigma |
|---------------------|---------------------|---------------------|
| FGET | FEEE65 | 4$$wr44 |
|---------------------|---------------------|---------------------|
| GRGT | FEF | f$§%§d54 |
|---------------------|---------------------|---------------------|
| TRG | 6FEFE4 | §%§d340 |
|---------------------|---------------------|---------------------|
I would like to map each columns and get this result:
|---------------------|---------------------|---------------------|
| Test | Wup | Sigma |
|---------------------|---------------------|---------------------|
| A | 65465 | 4wr44 |
|---------------------|---------------------|---------------------|
| B | 56 | fd54 |
|---------------------|---------------------|---------------------|
| C | 654564 | d340 |
|---------------------|---------------------|---------------------|
| Test | Wup | Sigma |
|---------------------|---------------------|---------------------|
| FGET | FEEE65 | 4$$wr44 |
|---------------------|---------------------|---------------------|
| GRGT | FEF | f$§%§d54 |
|---------------------|---------------------|---------------------|
| TRG | 6FEFE4 | §%§d340 |
|---------------------|---------------------|---------------------|
Here is my code, which is leading to very bad results:
let
Source = Table.NestedJoin(#"Snouba", {"Test"}, #"Snouba F", {"Test"}, "Snouba F", JoinKind.FullOuter),
#"Expanded Snouba F" = Table.ExpandTableColumn(Source, "Snouba F", {"Test", "Wup", "Sigma"}, {"Snouba F.Test", "Snouba F.Wup", "Snouba F.Sigma"})
in
#"Expanded Snouba F"


There must be something off with what you're trying because appending should do the trick.
You should be able to either use the GUI or define a new query as
let
Source = Table.Combine({Query1, Query2})
in
Source
which should look like this:
==================================================================
| Test | Wup | Sigma |
======================+====================+======================
| A | 65465 | 4wr44 |
|---------------------|--------------------|---------------------|
| B | 56 | fd54 |
|---------------------|--------------------|---------------------|
| C | 654564 | d340 |
|---------------------|--------------------|---------------------|
| FGET | FEEE65 | 4$$wr44 |
|---------------------|--------------------|---------------------|
| GRGT | FEF | f$§%§d54 |
|---------------------|--------------------|---------------------|
| TRG | 6FEFE4 | §%§d340 |
|---------------------|--------------------|---------------------|
If you need the column headers repeated, that's possible, but it's not clear if you need that or not.

Related

postgresql : self join with array

My question is about forming Postgres SQL query for below use case
Approach#1
I have a table like below where I generate the same uuid across different types(a,b,c,d) like mapping different types.
+----+------+-------------+
| id | type | master_guid |
+----+------+-------------+
| 1 | a | uuid-1 |
| 2 | a | uuid-2 |
| 3 | a | uuid-3 |
| 4 | a | uuid-4 |
| 5 | a | uuid-5 |
| 6 | b | uuid-1 |
| 7 | b | uuid-2 |
| 8 | b | uuid-3 |
| 9 | b | uuid-6 |
| 10 | c | uuid-1 |
| 11 | c | uuid-2 |
| 12 | c | uuid-3 |
| 13 | c | uuid-6 |
| 14 | c | uuid-7 |
| 15 | d | uuid-6 |
| 16 | d | uuid-2 |
+----+------+-------------+
Approach#2
I have a created two tables for id to type and then id to master_guid, like below
table1:
+----+------+
| id | type |
+----+------+
| 1 | a |
| 2 | a |
| 3 | a |
| 4 | a |
| 5 | a |
| 6 | b |
| 7 | b |
| 8 | b |
| 9 | b |
| 10 | c |
| 11 | c |
| 12 | c |
| 13 | c |
| 14 | c |
| 15 | d |
| 16 | d |
+----+------+
table2
+----+-------------+
| id | master_guid |
+----+-------------+
| 1 | uuid-1 |
| 2 | uuid-2 |
| 3 | uuid-3 |
| 4 | uuid-4 |
| 5 | uuid-5 |
| 6 | uuid-1 |
| 7 | uuid-2 |
| 8 | uuid-3 |
| 9 | uuid-6 |
| 10 | uuid-1 |
| 11 | uuid-2 |
| 12 | uuid-3 |
| 13 | uuid-6 |
| 14 | uuid-7 |
| 15 | uuid-6 |
| 16 | uuid-2 |
+----+-------------+
I want to get output like below with both approaches:
+----+------+--------+------------+
| id | type | uuid | mapped_ids |
+----+------+--------+------------+
| 1 | a | uuid-1 | [6,10] |
| 2 | a | uuid-2 | [7,11] |
| 3 | a | uuid-3 | [8,12] |
| 4 | a | uuid-4 | null |
| 5 | a | uuid-5 | null |
+----+------+--------+------------+
I have tried self-joins with array_agg on ids and grouping based on uuid but not able to get the desired output.
Use below query to populate data:
Approach#1
insert into table1 values
(1,'a','uuid-1'),
(2,'a','uuid-2'),
(3,'a','uuid-3'),
(4,'a','uuid-4'),
(5,'a','uuid-5'),
(6,'b','uuid-1'),
(7,'b','uuid-2'),
(8,'b','uuid-3'),
(9,'b','uuid-6'),
(10,'c','uuid-1'),
(11,'c','uuid-2'),
(12,'c','uuid-3'),
(13,'c','uuid-6'),
(14,'c','uuid-7'),
(15,'d','uuid-6'),
(16,'d','uuid-2')
Approach#2
insert into table1 values
(1,'a'),
(2,'a'),
(3,'a'),
(4,'a'),
(5,'a'),
(6,'b'),
(7,'b'),
(8,'b'),
(9,'b'),
(10,'c'),
(11,'c'),
(12,'c'),
(13,'c'),
(14,'c'),
(15,'d'),
(16,'d')
insert into table2 values
(1,'uuid-1'),
(2,'uuid-2'),
(3,'uuid-3'),
(4,'uuid-4'),
(5,'uuid-5'),
(6,'uuid-1'),
(7,'uuid-2'),
(8,'uuid-3'),
(9,'uuid-6'),
(10,'uuid-1'),
(11,'uuid-2'),
(12,'uuid-3'),
(13,'uuid-6'),
(14,'uuid-7'),
(15,'uuid-6'),
(16,'uuid-2')

demo: db<>fiddle
Using window function ARRAY_AGG allows you to aggregate your ids per groups (in your case the groups are your uuids)
SELECT
id, type, master_guid as uuid,
array_agg(id) OVER (PARTITION BY master_guid) as mapped_ids
FROM table1
ORDER BY id
Result:
| id | type | uuid | mapped_ids |
|----|------|--------|------------|
| 1 | a | uuid-1 | 10,6,1 |
| 2 | a | uuid-2 | 16,2,7,11 |
| 3 | a | uuid-3 | 8,3,12 |
| 4 | a | uuid-4 | 4 |
| 5 | a | uuid-5 | 5 |
| 6 | b | uuid-1 | 10,6,1 |
| 7 | b | uuid-2 | 16,2,7,11 |
| 8 | b | uuid-3 | 8,3,12 |
| 9 | b | uuid-6 | 15,13,9 |
| 10 | c | uuid-1 | 10,6,1 |
| 11 | c | uuid-2 | 16,2,7,11 |
| 12 | c | uuid-3 | 8,3,12 |
| 13 | c | uuid-6 | 15,13,9 |
| 14 | c | uuid-7 | 14 |
| 15 | d | uuid-6 | 15,13,9 |
| 16 | d | uuid-2 | 16,2,7,11 |
These arrays currently contain also the id of the current row (mapped_ids of id = 1 contains the 1). This can be corrected by remove this element with array_remove:
SELECT
id, type, master_guid as uuid,
array_remove(array_agg(id) OVER (PARTITION BY master_guid), id) as mapped_ids
FROM table1
ORDER BY id
Result:
| id | type | uuid | mapped_ids |
|----|------|--------|------------|
| 1 | a | uuid-1 | 10,6 |
| 2 | a | uuid-2 | 16,7,11 |
| 3 | a | uuid-3 | 8,12 |
| 4 | a | uuid-4 | |
| 5 | a | uuid-5 | |
| 6 | b | uuid-1 | 10,1 |
| 7 | b | uuid-2 | 16,2,11 |
| 8 | b | uuid-3 | 3,12 |
| 9 | b | uuid-6 | 15,13 |
| 10 | c | uuid-1 | 6,1 |
| 11 | c | uuid-2 | 16,2,7 |
| 12 | c | uuid-3 | 8,3 |
| 13 | c | uuid-6 | 15,9 |
| 14 | c | uuid-7 | |
| 15 | d | uuid-6 | 13,9 |
| 16 | d | uuid-2 | 2,7,11 |
Now for example id=4 contains an empty array instead of a NULL value. This can be achieved by using the NULLIF function. This gives NULL if both parameters are equal, else it gives out the first parameter.
SELECT
id, type, master_guid as uuid,
NULLIF(
array_remove(array_agg(id) OVER (PARTITION BY master_guid), id),
'{}'::int[]
) as mapped_ids
FROM table1
ORDER BY id
Result:
| id | type | uuid | mapped_ids |
|----|------|--------|------------|
| 1 | a | uuid-1 | 10,6 |
| 2 | a | uuid-2 | 16,7,11 |
| 3 | a | uuid-3 | 8,12 |
| 4 | a | uuid-4 | (null) |
| 5 | a | uuid-5 | (null) |
| 6 | b | uuid-1 | 10,1 |
| 7 | b | uuid-2 | 16,2,11 |
| 8 | b | uuid-3 | 3,12 |
| 9 | b | uuid-6 | 15,13 |
| 10 | c | uuid-1 | 6,1 |
| 11 | c | uuid-2 | 16,2,7 |
| 12 | c | uuid-3 | 8,3 |
| 13 | c | uuid-6 | 15,9 |
| 14 | c | uuid-7 | (null) |
| 15 | d | uuid-6 | 13,9 |
| 16 | d | uuid-2 | 2,7,11 |

Try this:
select
t1.id, t1.type, t1.master_guid, array_agg (distinct t2.id)
from
table1 t1
left join table1 t2 on
t1.master_guid = t2.master_guid and
t1.id != t2.id
group by
t1.id, t1.type, t1.master_guid
I don't come up with exactly the same results you listed, but I thought it was close enought that maybe there was a mistaken expectation on your side or only a small error on mine... either way, a potential starting point.
-- EDIT --
For approach #2, I think you just need to add an inner join to Table2 to get the GUID:
select
t1.id, t1.type, t2.master_guid,
array_agg (t2a.id)
from
table1 t1
join table2 t2 on t1.id = t2.id
left join table2 t2a on
t2.master_guid = t2a.master_guid and
t2a.id != t1.id
where
t1.type = 'a'
group by
t1.id, t1.type, t2.master_guid

Create Calculated Pivot from Several Query Results in PostgreSQL

I have question regarding how to make a calculated pivot table from several query results on PostgreSQL. I've managed to make three queries results but don't have any idea how to combine and calculate all the data into a single table. I've tried to google it but found out that most of the question is about how to make a pivot table from a single table, which I'm able to do using sum, case, and group by. Well, Here's the simplified version of my query results
Query from query 1 which contains gross value
| city | code | gross |
|-------|------|--------|
| city1 | 21 | 194793 |
| city1 | 25 | 139241 |
| city1 | 28 | 231365 |
| city2 | 21 | 282025 |
| city2 | 25 | 334458 |
| city2 | 28 | 410852 |
| city3 | 21 | 109237 |
Result from query 2 which contains positive adjustments
| city | code | adj_pos |
|-------|------|---------|
| city1 | 21 | 16259 |
| city1 | 25 | 13634 |
| city1 | 28 | 45854 |
| city2 | 25 | 18060 |
| city2 | 28 | 18220 |
Result from query 3 which contains negative adjustments
| city | code | adj_neg |
|-------|------|---------|
| city1 | 25 | 23364 |
| city2 | 21 | 27478 |
| city2 | 25 | 23474 |
And what I want to to is to create something like this
| city | 21_gross | 25_gross | 28_gross | 21_pos | 25_pos | 28_pos | 21_neg | 25_neg | 28_neg |
|-------|----------|----------|----------|--------|--------|--------|--------|--------|--------|
| city1 | 194793 | 139241 | 231365 | 16259 | 13634 | 45854 | | 23364 | |
| city2 | 282025 | 334458 | 410852 | | 18060 | 18220 | 27478 | 23474 | |
| city3 | 109237 | | | | | | | | |
or probably final calculation which come from gross + positive adjustment -
negative adjustment from each city on each code like this
| city | 21_nett | 25_nett | 28_nett |
|-------|---------|---------|---------|
| city1 | 211052 | 129511 | 277219 |
| city2 | 254547 | 329044 | 429072 |
| city3 | 109237 | 0 | 0 |
Any suggestion will be appreciated. Thank you!

I think the best you can achieve is to get the pivoting part as JSON - http://sqlfiddle.com/#!17/b7d64/23:
select
city,
json_object_agg(
code,
coalesce(gross,0) + coalesce(adj_pos,0) - coalesce(adj_neg,0)
) as js
from q1
left join q2 using (city,code)
left join q3 using (city,code)
group by city

Spark Dataframe Union giving duplicates

I have a base dataset, and one of the columns is having null and not null values.
so I do:
val nonTrained_ds = base_ds.filter(col("col_name").isNull)
val trained_ds = base_ds.filter(col("col_name").isNotNull)
When I print that out, I get clear separate of rows. But when I do,
val combined_ds = nonTrained_ds.union(trained_ds)
I get duplicate records of rows from nonTrained_ds, and the strange thing is, rows from trained_ds are no longer in the combined ds.
Why does this happen?
the values of trained_ds are:
+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700001|16 |
|0456700004|16 |
|0456700007|16 |
|0456700010|16 |
|0456700013|16 |
|0456700016|16 |
|0456700019|16 |
|0456700022|16 |
|0456700025|16 |
|0456700028|16 |
|0456700031|16 |
|0456700034|16 |
|0456700037|16 |
|0456700040|16 |
|0456700043|16 |
|0456700046|16 |
|0456700049|16 |
|0456700052|16 |
|0456700055|16 |
|0456700058|16 |
|0456700061|16 |
|0456700064|16 |
|0456700067|16 |
|0456700070|16 |
+----------+----------------+
the values of nonTrained_ds are:
+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700002|null |
|0456700003|null |
|0456700005|null |
|0456700006|null |
|0456700008|null |
|0456700009|null |
|0456700011|null |
|0456700012|null |
|0456700014|null |
|0456700015|null |
|0456700017|null |
|0456700018|null |
|0456700020|null |
|0456700021|null |
|0456700023|null |
|0456700024|null |
|0456700026|null |
|0456700027|null |
|0456700029|null |
|0456700030|null |
|0456700032|null |
|0456700033|null |
|0456700035|null |
|0456700036|null |
|0456700038|null |
|0456700039|null |
|0456700041|null |
|0456700042|null |
|0456700044|null |
|0456700045|null |
|0456700047|null |
|0456700048|null |
|0456700050|null |
|0456700051|null |
|0456700053|null |
|0456700054|null |
|0456700056|null |
|0456700057|null |
|0456700059|null |
|0456700060|null |
|0456700062|null |
|0456700063|null |
|0456700065|null |
|0456700066|null |
|0456700068|null |
|0456700069|null |
|0456700071|null |
|0456700072|null |
+----------+----------------+
the values of the combined ds are:
+----------+----------------+
|unique_no | running_id|
+----------+----------------+
|0456700002|null |
|0456700003|null |
|0456700005|null |
|0456700006|null |
|0456700008|null |
|0456700009|null |
|0456700011|null |
|0456700012|null |
|0456700014|null |
|0456700015|null |
|0456700017|null |
|0456700018|null |
|0456700020|null |
|0456700021|null |
|0456700023|null |
|0456700024|null |
|0456700026|null |
|0456700027|null |
|0456700029|null |
|0456700030|null |
|0456700032|null |
|0456700033|null |
|0456700035|null |
|0456700036|null |
|0456700038|null |
|0456700039|null |
|0456700041|null |
|0456700042|null |
|0456700044|null |
|0456700045|null |
|0456700047|null |
|0456700048|null |
|0456700050|null |
|0456700051|null |
|0456700053|null |
|0456700054|null |
|0456700056|null |
|0456700057|null |
|0456700059|null |
|0456700060|null |
|0456700062|null |
|0456700063|null |
|0456700065|null |
|0456700066|null |
|0456700068|null |
|0456700069|null |
|0456700071|null |
|0456700072|null |
|0456700002|16 |
|0456700005|16 |
|0456700008|16 |
|0456700011|16 |
|0456700014|16 |
|0456700017|16 |
|0456700020|16 |
|0456700023|16 |
|0456700026|16 |
|0456700029|16 |
|0456700032|16 |
|0456700035|16 |
|0456700038|16 |
|0456700041|16 |
|0456700044|16 |
|0456700047|16 |
|0456700050|16 |
|0456700053|16 |
|0456700056|16 |
|0456700059|16 |
|0456700062|16 |
|0456700065|16 |
|0456700068|16 |
|0456700071|16 |
+----------+----------------+

This did the trick,
val nonTrained_ds = base_ds.filter(col("primary_offer_id").isNull).distinct()
val trained_ds = base_ds.filter(col("primary_offer_id").isNotNull).distinct()

How can I make my dataframe sample in each run using apache spark

I have a peculiar scenario in which the sample obtained in two consecutive samplings are not consistent even when I've provided a seed value. I'm using the following code (Which was an outcome of a discussion here:
var conversionSample = sortedConversionSubset.sample(true, (sampleSize + 0.05), 3*x).limit((conversionCount * sampleSize).toInt)
var nonConversionSample = sortedNonConversionSubset.sample(true, (sampleSize + 0.05), 3*x).limit((nonConversionCount * sampleSize).toInt)
Here
'sampleSize' is a constant fraction value less than 0.8
'x' is a constant int, which represents xth iteration in a for loop
'conversionCount' and 'nonConversionCount' are int values representing number of rows in each subset
Now the observation being that in two successive runs the sample generated is different in both cases which was not the expected behavior.
sortedConversionSubset
+--------------------------------------+----------+
|clientid |Conversion|
+--------------------------------------+----------+
|02438b66-2de4-4765-bae3-de7453647ea7_1|1 |
|203865ed-f02a-4ed9-9098-82691de707a4_0|1 |
|203865ed-f02a-4ed9-9098-82691de707a4_1|1 |
|674e2337-aec5-434e-b56e-8c2efcc42894_1|1 |
|6d6036d3-c161-4f5d-8557-80b85dd87bd9_0|1 |
|6d6036d3-c161-4f5d-8557-80b85dd87bd9_1|1 |
|7797aba3-3eea-4556-856e-753812b4b551_0|1 |
|7797aba3-3eea-4556-856e-753812b4b551_1|1 |
|870ab2a5-0650-42b8-9e6f-bde3859f64fd_0|1 |
|870ab2a5-0650-42b8-9e6f-bde3859f64fd_1|1 |
|9b606693-4ffa-44a5-bd7c-cc6974ce3e83_0|1 |
|be218b72-c664-40cf-adf5-e3519095e941_0|1 |
|e7dc7fd9-32df-46a1-b3bd-793bbda09f6f_0|1 |
|eaf434da-6a8f-4ab0-a744-62bea663ed5e_0|1 |
|eaf434da-6a8f-4ab0-a744-62bea663ed5e_1|1 |
+--------------------------------------+----------+
sortedNonConversionSubset
+--------------------------------------+----------+
|clientid |Conversion|
+--------------------------------------+----------+
|03358d8f-9b9c-4258-9c99-234ab102c29b_1|0 |
|040d213c-e91a-42f4-9bf7-90671670dc17_0|0 |
|04fe5148-1c56-4c88-aed0-1f01220bffd6_0|0 |
|0ed2e621-9ba4-46f0-8793-a84d32538c39_0|0 |
|0f9bcf42-e7fa-49a0-9d75-6c9bbc38b4d5_0|0 |
|108c5478-abc0-44d9-968b-47f81c4f5a37_0|0 |
|129eb883-159d-49be-b8ae-9aa44a3e2919_0|0 |
|13e3d779-026b-4d12-8619-aa5fe6ca99ed_0|0 |
|14497295-eebd-44aa-9f26-fc5e4810fb54_0|0 |
|1855d96d-3647-4c4f-a20f-7e46f7635798_0|0 |
|1911caf0-a470-4898-9b62-57c604422727_0|0 |
|1b91b8dc-09b8-47e2-b892-f5c14b650019_0|0 |
|1dfa820c-77e0-4927-8a39-ecd8e842b09b_0|0 |
|1e48e346-4ada-4a8d-896b-7658cc2499cd_0|0 |
|252be902-4204-40a5-9d3c-dd3a7d0f0355_0|0 |
|2995b49d-525b-43e9-ab36-8b8910a4607c_0|0 |
|2bc06b59-4624-4ddd-87a3-ed04cba88233_0|0 |
|2d4538a5-20e6-4742-ae46-aad0a5ed3fff_0|0 |
|31563716-9380-4662-90e5-7f63a1ab9072_0|0 |
|34442a3e-0437-4c41-86fb-1ac55062993a_0|0 |
|35151629-2f86-4917-90d2-42daa5ae4f5c_0|0 |
|3c37e066-dff5-4bd9-84ab-b9e73f3f3fdd_0|0 |
|3e998096-3a4b-4b57-a1de-69d2dbd19abd_0|0 |
|3f8ace3c-d378-4423-97a0-3d9cf35ba256_0|0 |
|49a0cfb8-490f-4252-84fa-2b9e250e9333_0|0 |
|4c3f11fa-e3ba-4eb1-977a-06f034bf8a54_0|0 |
|4ee484f4-e877-44c3-9390-c4e4072c5dee_0|0 |
|4fa035b3-dcd5-40e1-9107-0a0c943ff597_1|0 |
|529704d2-5a60-4718-a03f-639e040f6634_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|57b47c74-b071-4278-89c9-f7b4cb1225d1_0|0 |
|58305773-f944-4039-8452-f5eb8d62f0cf_0|0 |
|58dfa9dd-43cf-4eb7-ade6-7235004a9815_0|0 |
|5b146218-9bb6-46f0-8c83-df131d78f591_0|0 |
|5ca3b5bc-35a9-42a5-bd37-a8fc94366dc6_0|0 |
|5d5f2ea0-aed9-4c2d-8c22-68859ec35e8e_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|64822b8c-009e-48ab-b6ca-1a7ece1106fa_0|0 |
|6b352714-af74-4773-854b-073e644e8684_0|0 |
|6e528e49-472e-48c7-baa9-edc25303e427_0|0 |
|73203f58-8be2-4716-b8f0-79c64400c57b_0|0 |
|741630e0-1c99-497d-a127-5c4c562952c5_0|0 |
|778e3b8a-2ca5-469a-9697-f646962e8308_0|0 |
|8029c542-d933-43fb-b359-f2438dcd5660_0|0 |
|8b06ba24-2af3-4eec-811a-4d1779f37876_0|0 |
|8fb43dff-260d-4ece-85e2-3bc2cb636ac1_0|0 |
|90f8a4cb-1956-43c4-ac7d-8c6514cd023a_0|0 |
|916f2e2a-6135-4004-8d54-d80b822ce394_0|0 |
|968a7ca3-1649-4586-9e60-b7e8565e708a_0|0 |
|a32782cc-8c4c-403b-aa83-09f1cec45fdb_0|0 |
|a63f44d5-a4d5-45a0-8a4b-cebf05df810b_0|0 |
|a6f958bc-e050-4216-b981-d51f1c0ff60d_0|0 |
|a7dba1bb-d7ff-44e6-9c4c-997ae59a2337_1|0 |
|ac33d675-d9cc-43b5-94fb-7d412773db14_0|0 |
|b1227816-9bf2-474f-8e82-5739acf6c895_0|0 |
|b1c27a2e-6efc-4869-880b-9ce0a4962edc_0|0 |
|b4ff6d43-cf0a-4f1d-9431-1edcb8ee1fb6_0|0 |
|b9e477ab-2065-42bb-832b-5d0e98ee05c7_0|0 |
|ba8c4efe-e71c-468c-b1bf-37efff596907_0|0 |
|c21eefc8-43d0-4be0-a252-b9fc4dbb7ad0_0|0 |
|c3785311-87c8-43bc-99a8-01d64f5eaa87_0|0 |
|c543bde7-deb8-4484-b0be-353c44baf6eb_1|0 |
|ca31e550-9d28-4628-bfe8-53648a2007f7_0|0 |
|cbc33697-20cb-4f8b-accd-0a6396a4ea41_0|0 |
|cc7810aa-08fc-44e7-acdc-ac948a28f9b9_0|0 |
|d1efdc7c-afb0-4995-bbbd-a76f731d2492_0|0 |
|d6a4b928-e576-41d7-9628-18709765199d_0|0 |
|d7311ec7-6c50-448d-8a6e-f690c3070d57_1|0 |
|d86b09f9-70a0-4101-a13b-129fe3a37b86_0|0 |
|d911be5b-aceb-45c8-a79e-73ccfa1b96f0_0|0 |
|db0c7b10-80f7-4071-aa53-fe0e2dc5ebce_0|0 |
|dce14c51-fa57-4e98-987d-708e2a9aa293_0|0 |
|dd026fb8-f818-4d1e-aaa4-4c9b3fd24994_0|0 |
|dfa9c55c-1e75-4010-be86-a6b1eb723672_0|0 |
|ea29f600-9e85-40f4-9f88-dcef46beb0c1_0|0 |
|eb5e58fc-eaac-4059-8ebc-1fab1ccf3555_1|0 |
|eb7568ab-83ac-45a7-bf4b-3b048d6c7c53_0|0 |
|f5b1cfc4-e397-4699-adab-0af6ee0e1b76_0|0 |
|facbfc8c-d477-4b27-bf15-52a56c26cbf6_0|0 |
|ffd03bca-ef40-4fa4-913e-73c002f29796_0|0 |
+--------------------------------------+----------+
1st Run Sample
+--------------------------------------+----------+
|clientid |Conversion|
+--------------------------------------+----------+
|203865ed-f02a-4ed9-9098-82691de707a4_1|1 |
|6d6036d3-c161-4f5d-8557-80b85dd87bd9_0|1 |
|6d6036d3-c161-4f5d-8557-80b85dd87bd9_1|1 |
|02438b66-2de4-4765-bae3-de7453647ea7_1|1 |
|7797aba3-3eea-4556-856e-753812b4b551_0|1 |
|870ab2a5-0650-42b8-9e6f-bde3859f64fd_0|1 |
|1dfa820c-77e0-4927-8a39-ecd8e842b09b_0|0 |
|252be902-4204-40a5-9d3c-dd3a7d0f0355_0|0 |
|2995b49d-525b-43e9-ab36-8b8910a4607c_0|0 |
|2bc06b59-4624-4ddd-87a3-ed04cba88233_0|0 |
|31563716-9380-4662-90e5-7f63a1ab9072_0|0 |
|5ca3b5bc-35a9-42a5-bd37-a8fc94366dc6_0|0 |
|5d5f2ea0-aed9-4c2d-8c22-68859ec35e8e_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|6b352714-af74-4773-854b-073e644e8684_0|0 |
|6e528e49-472e-48c7-baa9-edc25303e427_0|0 |
|6e528e49-472e-48c7-baa9-edc25303e427_0|0 |
|741630e0-1c99-497d-a127-5c4c562952c5_0|0 |
|03358d8f-9b9c-4258-9c99-234ab102c29b_1|0 |
|040d213c-e91a-42f4-9bf7-90671670dc17_0|0 |
|040d213c-e91a-42f4-9bf7-90671670dc17_0|0 |
|04fe5148-1c56-4c88-aed0-1f01220bffd6_0|0 |
|129eb883-159d-49be-b8ae-9aa44a3e2919_0|0 |
|1855d96d-3647-4c4f-a20f-7e46f7635798_0|0 |
|3c37e066-dff5-4bd9-84ab-b9e73f3f3fdd_0|0 |
|3e998096-3a4b-4b57-a1de-69d2dbd19abd_0|0 |
|3f8ace3c-d378-4423-97a0-3d9cf35ba256_0|0 |
|49a0cfb8-490f-4252-84fa-2b9e250e9333_0|0 |
|4fa035b3-dcd5-40e1-9107-0a0c943ff597_1|0 |
|4fa035b3-dcd5-40e1-9107-0a0c943ff597_1|0 |
|529704d2-5a60-4718-a03f-639e040f6634_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|778e3b8a-2ca5-469a-9697-f646962e8308_0|0 |
|8b06ba24-2af3-4eec-811a-4d1779f37876_0|0 |
+--------------------------------------+----------+
2nd Run Sample
+--------------------------------------+----------+
|clientid |Conversion|
+--------------------------------------+----------+
|02438b66-2de4-4765-bae3-de7453647ea7_1|1 |
|7797aba3-3eea-4556-856e-753812b4b551_0|1 |
|870ab2a5-0650-42b8-9e6f-bde3859f64fd_0|1 |
|870ab2a5-0650-42b8-9e6f-bde3859f64fd_1|1 |
|be218b72-c664-40cf-adf5-e3519095e941_0|1 |
|be218b72-c664-40cf-adf5-e3519095e941_0|1 |
|1dfa820c-77e0-4927-8a39-ecd8e842b09b_0|0 |
|252be902-4204-40a5-9d3c-dd3a7d0f0355_0|0 |
|2995b49d-525b-43e9-ab36-8b8910a4607c_0|0 |
|2bc06b59-4624-4ddd-87a3-ed04cba88233_0|0 |
|31563716-9380-4662-90e5-7f63a1ab9072_0|0 |
|5ca3b5bc-35a9-42a5-bd37-a8fc94366dc6_0|0 |
|5d5f2ea0-aed9-4c2d-8c22-68859ec35e8e_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|5f9ebf92-3b1b-4628-b949-44a32e6d3659_0|0 |
|6b352714-af74-4773-854b-073e644e8684_0|0 |
|6e528e49-472e-48c7-baa9-edc25303e427_0|0 |
|6e528e49-472e-48c7-baa9-edc25303e427_0|0 |
|741630e0-1c99-497d-a127-5c4c562952c5_0|0 |
|03358d8f-9b9c-4258-9c99-234ab102c29b_1|0 |
|040d213c-e91a-42f4-9bf7-90671670dc17_0|0 |
|040d213c-e91a-42f4-9bf7-90671670dc17_0|0 |
|04fe5148-1c56-4c88-aed0-1f01220bffd6_0|0 |
|129eb883-159d-49be-b8ae-9aa44a3e2919_0|0 |
|1855d96d-3647-4c4f-a20f-7e46f7635798_0|0 |
|3c37e066-dff5-4bd9-84ab-b9e73f3f3fdd_0|0 |
|3e998096-3a4b-4b57-a1de-69d2dbd19abd_0|0 |
|3f8ace3c-d378-4423-97a0-3d9cf35ba256_0|0 |
|49a0cfb8-490f-4252-84fa-2b9e250e9333_0|0 |
|4fa035b3-dcd5-40e1-9107-0a0c943ff597_1|0 |
|4fa035b3-dcd5-40e1-9107-0a0c943ff597_1|0 |
|529704d2-5a60-4718-a03f-639e040f6634_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|560f6978-028b-4a37-9f97-d97e93976bf7_0|0 |
|778e3b8a-2ca5-469a-9697-f646962e8308_0|0 |
|8b06ba24-2af3-4eec-811a-4d1779f37876_0|0 |
+--------------------------------------+----------+
The two samples being different could be a road blocker for me and just want to check how I could make these consistent

Per-table constants are not being substituted

Here is my table
| | Name | Vert | Horz | Area | Cost | USD |
|---+-------------+---------+-------+--------+------+-----|
| $ | $price = 75 | $Hi=2.9 | | | | |
| # | Kitchen | 4.160 | 3.630 | #ERROR | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
#+TBLFM: $5=$4*$Hi*2+$3*$Hi*2
Here is trace output:
Substitution history of formula
Orig: $4*$Hi*2+$3*$Hi*2
$xyz-> $4*(#UNDEFINED_NAME)*2+$3*(#UNDEFINED_NAME)*2
#r$c-> $4*(#UNDEFINED_NAME)*2+$3*(#UNDEFINED_NAME)*2
$1-> (3.630)*(#UNDEFINED_NAME)*2+(4.160)*(#UNDEFINED_NAME)*2
---------^
Error: #'s not allowed in this context
What's wrong? Why $Hi was not substituted?

Ah, found it by myself. Here is wrong row, and the fixed row:
| $ | $price = 75 | $Hi=2.9 | | | | |
| $ | price = 75 | Hi=2.9 | | | | |