I have two different datasets. I have done the fuzzy matching between the two data sets by running the function 'get_matches'. That gave me the perfect result in 3 columns by matching the data from full_name_to_be_check with the item_master data as below:
Matching Data from item master Score Index
126_SURGICAL SCRUB BRUSHSTER ILE(EO) DRY_MEDLINE INDUSTRIES UK LIMITED 93 0
127_SURGICAL SCRUB BRUSHCHLO RHEXIDINE_MEDLINE INDUSTRIES UK LIMITED 100 1
127_SURGICAL SCRUB BRUSHCHLO RHEXIDINE_MEDLINE INDUSTRIES UK LIMITED 88 1
128_SURGICAL SCRUB BRUSHPOVI DONE-IODINE_MEDLINE INDUSTRIES UK LIMITED 88 2
128_SURGICAL SCRUB BRUSHPOVI DONE-IODINE_MEDLINE INDUSTRIES UK LIMITED 100 2
129_SURGICAL SKIN MARKER/REGULAR BROAD TIP_FANNIN (UK) LTD 100 3
And my code is as below;
***data = pd.DataFrame([])
for i in po['full_name_to_be_check']:
t = get_matches(i,item_master['item_master'])
data = data.append(t)
data.to_excel('item_2.xlsx', index = False)***
But, I am struggling while I am trying to add the 'i' value means the looking value as a column into the data table within the loop. Can anyone help me with that, please?
Related
I have two tables one with data about franchise locations (Franchise Profile Info) and one with Award data. Each franchise location is given a certain number of awards they are allowed to give out per year. Each franchise location rolls up to a larger group depending on where in the country they are located. These tables are in a 1 to 1 relationship using Franchise ID. I am trying to create a matrix with the number of awards, total utilized, and percentage utilized rolled up to group with the ability to expand the groups and see individual locations. For some reason when I add the value fields a blank row is created. There are not any blank rows in either of the original tables so I'm not sure where this is coming from.
Franchise Profile Info table
ID
Franchise Name
Group
Street Address
City
State
164
Park's
West
12 Park Dr.
Los Angeles
CA
365
A & J
East
243 Whiteoak Rd
Stafford
VA
271
Otto's
South
89 Main St.
St. Augustine
FL
Award table
ID
Year
TotalAwards
Utilized
164
2022
16
12
365
2022
5
5
271
2022
22
17
This tables are in a relationship with a 1 to 1 match on ID
What I want the matrix to look like
Group
Total Awards
Utilized
%Awards Utilized
East
5
5
100%
West
16
12
75%
South
22
17
77%
Instead what I'm getting is this
Group
Total Awards
Utilized
%Awards Utilized
East
5
5
100%
West
16
12
75%
South
22
17
77%
0
0
0%
I can't for the life of me figure out where this row is coming from. I can add in the Group and Franchise name as rows but as soon as I add any of the value columns this blank row shows up.
You have a value on the many side that does not exist on the one side. You can read a full explanation here. https://www.sqlbi.com/articles/blank-row-in-dax/
I have data in the following format:
Building
Tenant
Type
Floor
Sq Ft
Rent
Term Length
1 Example Way
Jeff
Renewal
5
100
100
6
47 Fake Street
Tom
New
3
500
200
12
I need to create a visualisation in PowerBI that displays a pivot table of attribute by tenant, with a weighted averages (by square foot) column, like this:
Jeff
Tom
Weighted Average (by Sq Ft)
Building
1 Example Way
47 Fake Street
-
Type
Renewal
New
-
Floor
5
3
-
Sq Ft
100
500
433.3333333
Rent
100
200
183.3333333
Term Length (months)
6
12
11
I have unpivoted the original data, like this:
Tenant
Attribute
Value
Jeff
Building
1 Example Way
Jeff
Type
Renewal
Jeff
Floor
5
Jeff
Sq Ft
100
Jeff
Rent
100
Jeff
Term Length (months)
6
Tom
Building
47 Fake Street
Tom
Type
New
Tom
Floor
3
Tom
Sq Ft
500
Tom
Rent
200
Tom
Term Length (months)
12
I can almost create what I need from the unpivoted data using a matrix (as below), but I can't calculate the weighted averages column from that matrix.
Jeff
Tom
Building
1 Example Way
47 Fake Street
Type
Renewal
New
Floor
5
3
Sq Ft
100
500
Rent
100
200
Term Length (months)
6
12
I can also create a table with my attributes as headers (instead of in a column). This displays the right values and lets me calculate weighted averages (as below).
Building
Type
Floor
Sq Ft
Rent
Term Length (months)
Jeff
1 Example Way
Renewal
5
100
100
6
Tom
47 Fake Street
New
3
500
200
12
Weighted Average (by Sq Ft)
-
-
-
433.3333333
183.3333333
11
However, it's important that these values are displayed vertically instead of horizontally. This is pretty straightforward in Excel, but I can't figure out how to do it in PowerBI. I hope this is clear. Can anyone help?
Thanks!
I am working on a report that counts stores with different types of beverages. I am trying to get a distinct count of stores that are selling 4 or more Powerade flavors and two or more Coca-Cola flavors while maintaining a count of stores that are purchashing other products (Sprite, Dr. Pepper, etc.).
My data table is BEVSALES and the data looks like:
CustomerNo Brand Flavor
43 PWD Fruit Punch
37 Coca-Cola Vanilla
43 PWD Mixed Bry
37 Coca-Cola Cherry
44 Sprite Tropical Mix
43 PWD Strawberry
43 PWD Grape
44 Coca-Cola Cherry
17 Dr. Pepper Cherry
I am trying to make the data give me a distinct count of customers with filters that have PWD>=4 and Coca-Cola>=2, while keeping the customer count of Dr. Pepper and Sprite at 1 each. (1 customer purchasing PWD, 1 customer Purchasing Coca-Cola, etc.)
The best measure that I have been able to find is
= SUMX(BEVSALES, 1*(FIND("PWD",BEVSALES[Brand],,0)))
but I don't know how to put it together so the formula counts the stores that have more than 4 PWD and 2 Coca-Cola flavors. Any ideas?
The easiest way would be to do this in a separate query. Go to the query design and click on edit. Then chose your table and group by column Brand and distinctcount the column Flavor. The result should look like this (Maybe as a new table):
GroupedBrand DistinctCountFlavor
PWD 4
Coca-Cola 2
Sprite 1
Dr. Pepper 1
Now you can access the distinct count of the flavors by brands. With an IIF() statement you can check for >=4 at PWD and so on...
I have a dataset OvertimeHours with EMPLID, checkdate and NumberOfHours (and other fields). I need a running total NumberOfHours for each employee by checkdate. I tried using the Quick Measure option but that only allows for a single column and I have two. I do not want the measure to recalculate when filters are applied. Ultimately what I am trying to do is identify the records for the first 6 hours of overtime worked on each check so that they can get a category of OCB and all overtime over the first 6 hours is OTP and it does not have to be exact (as demonstrated in the output below). I have only been working with Power BI for about a month and this is a pretty complex (for me) formula to figure out...
EMPLID CheckDate WkDate NumberOfHours RunningTotal Category
124 1/1/19 12/20/18 5 5 OCB
124 1/1/19 12/21/18 9 14 OTP
125 1/1/19 12/20/18 3 3 OCB
125 1/1/19 12/20/18 2 5 OCB
125 1/1/19 12/22/18 2 7 OTP
124 1/15/19 1/8/19 3 3 OCB
*Edited to add the WkDate.
Edit:
I have tweaked my query so that I have the running total and a sequential counter now:
Using the first 12 records, I am looking to get the following results:
I can either do it in a query if that is the easiest way or if there is a way to use DAX in PowerBI with this dataset now that I have the sequential piece, I can do that too.
I got it in the query:
select r.CheckDate,
r.EMPLID,
case
when PayrollRunningOTHours <= 6
then PayrollRunningOTHours
else 6
end as OCBHours,
case
when PayRollRunningOTHours > 6
then PayRollRunningOTHours - 6
end as OTPHours
from #rollingtotal r
inner
join lastone l
on r.CheckDate = l.CheckDate
and r.EMPLID = l.EMPLID
and r.OTCounter = l.lastRec
order by r.emplid,
r.CheckDate,
r.OTCounter
I gathered some NBA players' data of their triple-double games, and would like to find out who got the most explosive data on average.
The source is "Basketball Reference - Player Game Finder - Triple Doubles".(Sorry that I can't post the direct url because of the lack of reputation)
So I generated a table summarizing descriptive statistics (e.g. count mean) for several variables (pts trb ast stl blk) usingļ¼
tabstat pts trb ast stl blk, statistics(count mean) format(%9.1f) by(player)
What I get is the following table:
tabstat result:
How can I tell Stata to filter the players by count >= 10 (who got 10 or more triple-doubles ever) as a column then sort the table by pts and get:
Ideal result:
Like above, I would say Michael Jordan and James Harden are the Top 2 most explosive triple-double players and Darrell Walker is the most economic one.
Do study https://stackoverflow.com/help/mcve on how to present an example other people can work with straight away. Also, avoiding sports-specific jargon that won't be universally comprehensible and focusing more on the general programming problem would help. Fortunately, what you want seems clear nevertheless.
To do this you need to create a variable defining the order desired in advance of your tabstat call. To get it (value) labelled as you wish, use labmask (search labmask then download from the Stata Journal location given).
Here is some technique.
sysuse auto, clear
egen mean = mean(weight), by(rep78)
egen count = count(weight), by(rep78)
egen group = group(mean rep78) if count >= 5
replace group = -group
labmask group, values(rep78)
label var group "`: var label rep78'"
tabstat mpg weight , by(group) s(count mean) format(%1.0f)
Summary statistics: N, mean
by categories of: group (Repair Record 1978)
group | mpg weight
-------+--------------------
2 | 8 8
| 19 3354
-------+--------------------
3 | 30 30
| 19 3299
-------+--------------------
4 | 18 18
| 22 2870
-------+--------------------
5 | 11 11
| 27 2323
-------+--------------------
Total | 67 67
| 21 3030
----------------------------
Key details:
The grouping variable is based not only on the means you want to sort on but also on the original grouping variable, just in case there are ties on the means.
To get ordering from highest mean downwards, the grouping variable must be negated.
tabstat doesn't show variable labels in the body of the table. (Usually there wouldn't be enough space for them.)