How would I do the Secondary sorting on a bar chart, for each individual date ?
for example, I have data as follows
Date Type Value
1/1/2020 A1 4
1/1/2020 A2 2
1/1/2020 A3 9
1/1/2020 A4 5
1/1/2020 A5 7
2/1/2020 A1 7
2/1/2020 A2 5
2/1/2020 A3 0
2/1/2020 A4 3
2/1/2020 A5 1
3/1/2020 A1 3
3/1/2020 A2 5
3/1/2020 A3 7
3/1/2020 A4 9
3/1/2020 A5 8
now I need to plot daily bar chart only showing the top three maximum values of individual dates? i.e., the chart would be
Date Type Value
1/1/2020 A3 9
1/1/2020 A4 5
1/1/2020 A5 7
2/1/2020 A1 7
2/1/2020 A2 5
2/1/2020 A4 3
3/1/2020 A3 7
3/1/2020 A4 9
3/1/2020 A5 8
i.e. individual date top three, not like first sum up A1,A2,A3,A4,A5 for each date, and then sorting based on the cumulative sum.
You should be able to achieve the sorting you need through having Date as the dimension and Type as the breakdown dimension.
You should be able to then sort by Date and then secondary sort by type.
Restricting to 3 per date however is something you'd need to do in your data source as Data Studio can't currently do that.
Related
In Splunk, I have a search producing a result table like this:
_time
A
B
C
2022-10-19 09:00:00
A1
B1
C1
2022-10-19 09:00:00
A2
B2
C2
2022-10-19 09:10:20
A3
B3
C3
Now, for each row, I want to run a second search, using the _time value as input parameter.
For above row 1 and 2 (same _time value), the result of the second search would be:
_time
D
E
2022-10-19 09:00:00
D1
E1
For above row 3, the result of the second search would be:
_time
D
E
2022-10-19 09:10:20
D3
E3
And now I want to output the results in a common table, like this:
_time
A
B
C
D
E
2022-10-19 09:00:00
A1
B1
C1
D1
E1
2022-10-19 09:00:00
A2
B2
C2
D1
E1
2022-10-19 09:10:20
A3
B3
C3
D3
E3
I experimented with join, append, map, appendcols and subsearch, but I am struggling both with the row-by-row character of the second search and with pulling to data together into one common table.
For example, appendcols simply tacks one result table onto another, even if they are completely unrelated and differently shaped. Like so:
_time
A
B
C
D
E
2022-10-19 09:00:00
A1
B1
C1
D1
E1
2022-10-19 09:00:00
A2
B2
C2
-
-
2022-10-19 09:10:20
A3
B3
C3
-
-
Can anybody please point me into the right direction?
I'm very new to SQL and I've I simply cannot work out a method for the following:
I have this table with start dates of the codes
Row Code Start Date Product
1 A1 2020-01-01 X
2 A1 2020-05-15 Y
3 A2 2020-02-02 X
4 A3 2020-01-31 Z
5 A3 2020-02-15 Y
6 A3 2020-12-31 X
Ultimately I need to be able to query another table and find out what Product a code was on a certain date, so Code A1 on 2020-01-10 was = X, but Code A1 today is = Y
I think I can work out how to use a between statement in the where clause, but I cannot work out how to Alter the table to have an End Date so it looks like this:
Row Code Start_Date Product End_Date
1 A1 2020-01-01 X 2020-05-14
2 A1 2020-05-15 Y NULL
3 A2 2020-02-02 X NULL
4 A3 2020-01-31 Z 2020-02-14
5 A3 2020-02-15 Y 2020-12-30
6 A3 2020-12-31 X NULL
Please note the database does not have an End_Date field
I think you want lead():
select t.*,
dateadd(day, -1,
lead(start_date) over (partition by code order by start_date)
) as end_date
from t;
Note: I would recommend not subtracting one day for the end date, so the end date is non-inclusive. This makes the end date the same as the next start date, which I find is easier to ensure that there are no gaps or overlaps in the data.
If I want to merge the table with 2 other tables b,c
where table a contains columns:( Parent, Style, Ending_Date, WeekNum, Net_Requirment)
tables and calculate how much is required to make product A in a certain date.
The table should like the BOM (Bill of Material)
Can it be applied by pandas?
table b represent the demand for product A per date:
Style Date WeekNum Quantity
A 24/11/2019 0 600
A 01/12/2019 1 500
table c represent Details and quantity used to make product A:
Parent Child Q
A A1 2
A1 A11 3
A1 A12 2
so table a should be filled like this:
Parent Child Date WeekNum Net_Quantity
A A1 24/11/2019 0 1200
A1 A11 24/11/2019 0 3600
A1 A12 24/11/2019 0 2400
A A1 01/12/2019 1 1000
A1 A11 01/12/2019 1 3000
A1 A12 01/12/2019 1 2000
Welcome, in order to properly merge these tables and the rest you would have to have a common key to merge on. What you could do is add said key to each table like this:
data2 = {'Parent':['A','A1','A1'], 'Child':['A1','A11','A12'],
'Q':[2,3,2], 'Style':['A','A','A']}
df2 = pd.DataFrame(data2)
After this you can do a left join on the first table and then you can have multiple rows for the same date. So essentially this:
(notice if you do a left join, your left table will create as many duplicate rows as needed tu suffice the matching key on the right table)
data = {'Style':['A','A'], 'Date':['24/11/2019', '01/12/2019'],
'WeekNum':[0,1], 'Quantity':[600,500]}
df = pd.DataFrame(data)
mergeDf = df.merge(df2,how='left', left_on='Style', right_on='Style')
mergeDf
Then to calculate:
test['Net_Quantity'] = test.Quantity*test.Q
test.drop(['Q'], axis = 1,inplace=True)
result:
Style Date WeekNum Quantity Parent Child Net_Quantity
0 A 24/11/2019 0 600 A A1 1200
1 A 24/11/2019 0 600 A1 A11 1800
2 A 24/11/2019 0 600 A1 A12 1200
3 A 01/12/2019 1 500 A A1 1000
4 A 01/12/2019 1 500 A1 A11 1500
5 A 01/12/2019 1 500 A1 A12 1000
The code as written below returns the appropriate customers, lockers, units and balance. However, when I add in the commented-out code it reiterates each customer's data for each club even though each customer can only be a member of one club.
USE db1
GO
SELECT [db1].[dbo].[Customer].[CustomerNumber] AS 'Customer No.'
-- ,A. ClubID AS 'Club ID No.'
,(SELECT CONCAT (SI.Locker, '-', SI.Frequency)) AS Locker
,SI.Unit AS Unit
--,[db2].[dbo].[vueClub].Club_aka AS Club
,[db1].[dbo].[Customer_Balance].[CurrentBalance]
FROM [db1].[dbo].[Customer_Balance]
JOIN [db1].[dbo].[Customer]
ON [db1].[dbo].[Customer_Balance].POSCusNo = Customer.CustomerNumber
JOIN [SQLSrv01].[ db3].[dbo].[md_Table_1] AS D
ON D.Contract_no = [db1].[dbo].[Customer_Balance]. POSCusNo
JOIN [SQLSrv01].[ db2].[dbo].[vueSoldLockers] AS SI
ON SI.CustomerID = [db1].[dbo].[Customer].CustomerID
--JOIN [db2].[dbo].[vueClub] AS A
--ON [db1].[dbo].[Customer].SiteID = A.SiteID
WHERE [db1].[dbo].[Customer_Balance].StatusCode = '1234'
ORDER BY Customer.CustomerNumber ASC
So if I run it as is I get:
Customer No. Locker Unit Current Balance
1 315 A1 456.00
2 316 A3 1204.70
3 317 B2 335.60
4 318 B4 1500.30
But if I include the commented-out code I get:
Customer No. Club ID No Locker Unit Club Current Balance
1 4 315 A1 Tigers 456.00
1 3 315 A1 Lions 456.00
2 4 316 A3 Tigers 1204.70
2 3 316 A3 Lions 1204.70
3 4 317 B2 Tigers 335.60
3 3 317 B2 Lions 335.60
4 4 318 B4 Tigers 1500.30
4 3 318 B4 Lions 1500.30
Is it because I don't have the JOIN set up properly?
Customer No. Club ID No Locker Unit Club Current Balance
1 4 315 A1 Tigers 456.00
1 3 315 A1 Lions 456.00
You are joining customer to vueClub on SiteID. Looks like the site customer 1 is in, has 2 clubs (3, 4)
This question already has answers here:
Writing a routine to create sequential records
(3 answers)
Closed 8 years ago.
I am trying to write a routine which will take identify the most recent record in 'table 1' and compare it to a date which i store on another table ('table 2'). Once the comparison is made, I would like to create a routine (most likely using a loop) which will duplicate that same record from 'table 1' for each day falling between the range of the date in 'table 1' and the date in 'table 2'. For clarity purposes, if the date in 'table 1' is 4 September and the date identified in 'table 2' is 8 September, then i would like to create a routine that will duplicate the record in 'table 1' dated september 4, 4 times.. Sept 5, Sept 6, Sept7, and September 8. Sorry if this is a bit confusing but I am a bit in the dark as to the best way to do this. Thansk.
Tred to create an example from your question please create a result like you would like it to look like (I don't really get what are you aiming for sorry)
**TABLE1**
ID NAME DATE
1 A1 2014-09-01
2 A2 2014-09-02
3 A3 2014-09-03
4 A4 2014-09-04
5 A5 2014-09-05
6 A6 2014-09-06
7 A7 2014-09-07
8 A8 2014-09-08
9 A9 2014-09-09
10 A10 2014-09-10
**TABLE2**
ID NAME DATE
1 B1 2014-09-03
2 B2 2014-09-04
3 B3 2014-09-06
4 B4 2014-09-08
5 B5 2014-09-10
**RESULT** (???)
T1.ID T1.NAME T1.DATE T2.ID T2.NAME T2.DATE