I have the following data structure in my qlik sense desktop (version june 2019):
Time Reference Relevant
01.01.2019 1 1
01.01.2019 2 0
01.02.2019 3 0
01.02.2019 4 0
And I want to built a line chart, which displays the %-of the Total occuring references per day.
So the values would be:
01.01.2019 0.5
01.02.2019 0
What do I have to write in the formel editor? The expression: Sum(total [Relevant])/Count(total [Reference])is not working.
What is the trick to combine a sum and count with respect to the date?
To get the count of References that have Relevant=1 you can do Count({<Relevant={1}>}Reference) so then you can do a percentage as Count({<Relevant={1}>}Reference)/Count(Reference).
However since the data in your scenario is very simple, you could actually just do sum(Relevant)/count(Reference) - no need for the total as you're not including the Reference in the second table.
If you did want a table to show what % that day was relevant whilst still showing the Reference then you'd probably want something more like Count(TOTAL<Time> {<Relevant={1}>}Reference)/Count(TOTAL<Time> Reference) or just sum(TOTAL<Time> Relevant)/count(TOTAL<Time> Reference) in your simple scenario.
Related
I'm working on a project that involves building an automated tool for our pricing team to look at the effects of their pricing changes on demand. I'm writing in Python and using SQL to query our DB2 data sources.
The idea is to allow the pricing team to tell the tool the line they want to check and the week number that they made a price change (week_id in the form 202219 in case it is needed), the program will then calculate the number of completed weeks between runtime and the price change to determine how many weeks before and after the price change to return for comparison (this variable is called deltaWeek).
My thought right now is to use a CTE to calculate the total demand by week, then I want to reference that CTE to gather week_id batches the size of deltaWeek and SUM() the total quantity for each batch.
I have the CTE query working, and the output of the query is good, and assuming the week of the price change was week 12, deltaWeek = 6, and the quantity is all the same (which it isn't in reality, but it makes it easy) a condensed output looks like this (it excludes the week of the price change on purpose)
ROW_NUM
WEEK_ID
QUANTITY
1
201906
10
2
201907
10
3
201908
10
4
201909
10
5
201910
10
6
201911
10
7
201913
10
8
201914
10
9
201915
10
10
201916
10
11
201917
10
12
201918
10
Is there a way in DB2 to reference this CTE and return something that would look like this
BATCH
QUANTITY
1
60
2
60
where BATCH 1 represents SUM(QUANTITY) for ROW_NUM 1-6 FROM WEEKLY_TOTALS_CTE and BATCH 2 is similar for ROW_NUM 7-12
More generally, because deltaWeek, and thus the number of weeks in any given batch will depend on when the tool is ran, I need to total from ROW 1 - deltaWeek, then deltaWeek+1 - deltaWeek*2, etc.. I have working python functions to make SQL templates using parameters so I can pass deltaWeek into the query if I can figure out the logic to make this query work.
If this is a terrible idea to try to make work, I guess I can just run the query using pd.read_sql and then use iloc[] to do the batch aggregation, but I feel like it should be able to be done all in the query, maybe?
Thank you for any help/reference.
I have a table with sales information at the transaction level. We want to institute a new model where we compensate sales reps if a customer has been makes a purchase after more than a year of dormancy. To figure out how much this would have cost historically, I want to add a column with a flag for whether or not each purchase was the Buyer's first in the past 365 days. What I'd like to do is a rowcount in Powerpivot, for all sales made by that customer in the past 365 days, and wrap it in an IF to set the result to 0 or 1.
Example:
Order Date Buyer First Purchase in Year?
1/1/2015 1 1
1/2/2015 2 1
2/1/2015 1 0
4/1/2015 2 0
3/1/2016 2 1
5/1/2017 2 1
Any assistance would be greatly appreciated.
Excellent business use case! It's quite relevant in the business world.
To break this down for you, I will create 3 columns: 2 with some calculations, and 1 with the result. Once you understood how I did this, you can combine all 3 column formulas and make a single column for your dataset, if you like.
Here's a picture of the results:
So here's the 3 columns that I created:
Last Purchase - in order to run this calculation, you need to know when the buyer made their last purchase.
CALCULATE(MAX([Order Date]),FILTER(Table1,[Order Date]<EARLIER([Order Date]) && [Buyer]=EARLIER([Buyer])))
Days Since Last Purchase - now you can compare the Last Purchase date to the current Order Date.
DATEDIFF([Last Purchase],[Order Date],DAY)
First Purchase in 1 Year - finally, the results column. This simply checks to see if it has been more than 365 days since the last purchase OR if the last purchase column is blank (which means it was the first purchase), and creates the flag you want.
IF([Days Since Last Purchase]>365 || ISBLANK([Days Since Last Purchase]),1,0)
Now, you can easily combine the logic of these 3 columns into a single column and get what you want. Hope this helps!
One note I wanted to add is that for this type of analysis it's not a wise move to do row counts as you had originally suggested, as your dataset can easily expand later on (what if you wanted to add more attribute columns?) and then you would have problems. So this solution that I shared with you is much more robust.
The data I am working with is oil and gas production data. The production table uniquely identifies each well and contains a time series of production values. I want to be able to calculate a column that contains the month number occurrence of production for every well in the production table. This needs to be a calculation, so I can graph the production for various wells based on the production month, not the calendar month. (I want to compare well performance across wells over the life of wells.) Also note that there could be gaps in the production data so you can't depend on having twelve months of sequential production for each well.
I tried using the answer in this postRankValues but the calculation would never finish. I have over 4 million rows of production data.
In the table shown below, the values shown in ProdMonth is what I need to calculate based on their time occurrence shown in ProdDate. This needs to be performed as a row calculation for each unique WellId
Thanks.
WellID ProdDate ProdMonth
1 12/1/2011 1
1 1/1/2012 2
1 2/1/2012 3
1 3/1/2012 4
… … …
1 11/1/2012 12
2 3/1/2014 1
2 4/1/2014 2
2 5/1/2014 3
2 6/1/2014 4
2 7/1/2014 5
… … …
2 2/1/2014 12
I would create a new date table that has a row for each day (the granularity of your data). I would then add to that table the ProdMonth column. This will ensure you have dates for all days (even if there are gaps in the well reporting data). Then you can use a relationship between the well production data and the Date table on the ProdDate field. Then if you pull in the ProdMonth from the date table, you'll have a list of all of the ProdMonths (hint: you may need to select 'show values with no data' on the field right click menu in the fields well). Then if you add to the same visualization WellID you should be able to see which wells were active in which ProdMonth. If WellID is a number, you might need do use the 'do not summarize' feature on the WellID to get the result you desire.
I posted this question on the PowerPivotPro and Tom Allan provided the DAX formula I needed. First step was to calculate a field that concatenated Year and Month (YearMonth). Then utilized the RANKXX function as such:
= RANKX ( FILTER ( Data, [WellID] = EARLIER ( [WellID] ) ), [YearMonth], , 1, DENSE )
That did the trick and performed fairly quickly on 12mm rows.
i have a table xyz, with three columns rcvr_id,mth_id and tpv. rcvr_id is an id given to a customer, mth_id is a column which stores the month number( mth_id is calculated as (2012-1900) * 12 + 1,2,3.. ( depending on the month). So for example Dec 2011 will have month_id of 1344, Jan 2012 1345 etc. Tpv is a variable which shows the customers transaction amount.
Example table
rcvr_id mth_id tpv
1 1344 23
2 1344 27
3 1344 54
1 1345 98
3 1345 102
.
.
.
so on
P.S if a customer does not have a transaction in a given month, his row for that month wont exist.
Now, the question. Based on transactions for the months 1327 to 1350, i need to classify a customer as steady or sporadic.
Here is a description.
The above image is for 1 customer. i have millions of customers.
How do i go about it? I have no clue how to identify trends in sql .. or rather how to do it the best way possible.
ALSO i am working on teradata.
Ok i have found out how to get standard deviation. Now the important question is : How do i set a standard deviation limit on my own? i just cant randomly say "if standard dev is above 40% he is sporadic else steady". I thought of calculating average of standard deviation for all customers and if it is above that then he is sporadic else steady. But i feel there could be a better logic
I would suggest the STDDEV_POP function - a higher value indicates a greater variation in values.
select
rcvr_id, STDDEV_POP(tpv)
from yourtable
group by rcvr_id
STDDEV_POP is the function for Standard Deviation
If this doesn't differentiate enough, you may need to look at regression functions and variance.
Wondering if anyone can help with the code for this.
I want to query the data and get 2 entries, one for YTD previous year and one for this year YTD.
Only way I know how to do this is as 2 separate queries with where clauses.. I would prefer to not have to run the query twice.
One column called DatePeriod and populated with 2011 YTD and 2012YTD, would be even better if I could get it to do 2011YTD, 2012YTD, 2011Total, 2012Total... though guessing this is 4 queries.
Thanks
EDIT:
In response to help clear a few things up:
This is being coded in MS SQL.
The data looks like so: (very basic example)
Date | Call_Volume
1/1/2012 | 4
What I would like is to have the Call_Volume summed up, I have queries that group it by week, and others that do it by month. I could pull all the dailies in and do this in Excel but the table has millions of rows so always best to reduce the size of my output.
I currently group by Week/Month and Year and union all so its 1 output. But that means I have 3 queries accessing the same table, large pain, very slow not efficient and that is fine but now I also need a YTD so its either 1 more query or if I could find a way to add it to the yearly query that would ideal:
So
DatePeriod | Sum_Calls
2011 Total | 40
2011 YTD | 12
2012 Total | 45
2012 YTD | 15
Hope this makes any sense.
SQL is built to do operations on rows, not columns (you select columns, of course, but aggregate operations are all on rows).
The most standard approach to this is something like:
SELECT SUM(your_table.sales), YEAR(your_table.sale_date)
FROM your_table
GROUP BY YEAR(your_table.sale_date)
Now you'll get one row for each year on record, with no limit to how many years you can process. If you're already grouping by another field, that's fine; you'll then get one row for each year in each of those groups.
Your program can then iterate over the rows and organize/render them however you like.
If you absolutely, positively must have columns instead, you'll be stuck with something like this:
SELECT SUM(IF(YEAR(date) = 2011, sales, 0)) AS total_2011,
SUM(IF(YEAR(date) = 2012, total_2012, 0)) AS total_2012
FROM your_table
If you're building the query programmatically you can add as many of those column criteria as you need, but I wouldn't count on this running very efficiently.
(These examples are written with some MySQL-specific functions. Corresponding functions exist for other engines but the syntax would be a little different.)