I have 3 measures:(A,B,C)
Mesaure c has its own formula : if(A>B) then c=A-B
else c=0
This formula is run for every records .Thats ok!
The problem is that I want to get sum of the measure c,the above formula work.
A B C
5 2 3
6 8 0
total : 11 10 1
In above example the result that shown is "1" But I want to show "3"
what I expected id :
A B C
5 2 3
6 8 0
total : 11 10 3
Related
I have a dataset as this below, want to pick up the name which applied more than 3 times every month.
Which is passion, otherwise is n-passion
Month Name Applied
4 a 3
4 b 2
4 c 4
5 a 3
5 b 4
5 c 2
6 a 5
6 b 7
6 c 0
enter image description here
Wanted output as below
Name Status
a passion
b n-passion
c c-passion
enter image description here
enter image description here
How to achive this?
This question already has an answer here:
increment a value each time the next row is different from the previous one
(1 answer)
Closed 3 months ago.
I wanted to create a new column, let say named "group id" on the basis of:
compare the nth row with (n-1)th row.
if both the records are equal then in a "group id", previous "group id" is copied
If these records are not equal, then 1 should be added to "group id column".
I wanted to have the result in the following way:
The expected result
Column A
Column B
6-Aug-10
0
30-Aug-11
1
31-Aug-11
2
31-Aug-11
2
6-Sep-12
3
30-Aug-13
4
Looking for the result, similar to this excel function
=IF(T3=T2, U2, U2+1)
you can use ngroup:
df['Group ID']=df.groupby('DOB').ngroup()
#according to your example
df['Group ID']=df.groupby('Column A').ngroup()
Use factorize - consecutive groups are not count separately like compare shifted values with Series.cumsum and subtract 1:
print (df)
Column A Column B
0 6-Aug-10 0
1 30-Aug-11 1
2 31-Aug-11 2
3 31-Aug-11 2
4 6-Sep-12 3
5 30-Aug-13 4
6 30-Aug-11 5 <- added data for see difference
7 31-Aug-11 6 <- added data for see difference
df['Group ID1'] = pd.factorize(df['Column A'])[0]
df['Group ID2'] = df['Column A'].ne(df['Column A'].shift()).cumsum().sub(1)
print (df)
Column A Column B Group ID1 Group ID2
0 6-Aug-10 0 0 0
1 30-Aug-11 1 1 1
2 31-Aug-11 2 2 2
3 31-Aug-11 2 2 2
4 6-Sep-12 3 3 3
5 30-Aug-13 4 4 4
6 30-Aug-11 5 1 5
7 31-Aug-11 5 2 6
I would like to turn a data frame like this:
DF
Nrow
a
1
5
2
6
3
7
4
11
5
16
Into this:
DF
Nrow
a
b
1
5
NA
2
6
NA
3
7
2
4
11
5
5
16
9
Column 'b' is calculated as the value from column 'a' minus another value from column 'a', in [row-2]. For example b4 = a4-a2.
I have had no success so far with indexing or loops. Is there a tool or command for this or some obvious notation that I am missing? I need to do this continuously without splitting into groups.
I need to create a new transport ID based on the cumulative sum of the volume being transported. Let´s say that originally everything was transported in truck A with a capacity of 25. Now I want to assign these items to shipments with truck B (Capacity 15).
The only real constraint is amt shipped cannot exceed capacity.
I can´t post a picture because of the restrictions...but the overall set up would be like this:
Old Trans # Volume New Trans # Cumulative Volume for Trans
1 1
1 9
1 3
1 7
1 4
2 9
2 10
3 8
3 5
3 9
4 4
4 6
4 8
5 9
5 1
5 5
5 8
6 3
6 4
6 3
6 4
6 4
6 7
7 7
7 10
7 4
8 10
8 6
8 7
9 4
9 9
9 6
10 7
10 4
10 1
10 1
10 5
10 2
11 9
11 3
11 9
12 8
12 5
12 9
13 9
Expected output would be that the first three entries would result in a new shipment ID of 1;the next two entries would result in a new shipment ID of 2;and so on... I´ve tried everthing that I know(excluding VBA): Index/lookup/if functions. My VBA skills are very limited though.Any tips?? thanks!
I think I see what you're trying to do here, and just using an IF formula (and inserting a new column to keep track):
In the Columns C and D, insert these formulas in row 3 and copy down (changing 15 for whatever you want your new volume capacity to be):
Column C: =IF(B3+C2<15,B3+C2,B3)
Column D: =IF(B3+C2<15,D2,D2+1)
And for the cells C2 and D2:
C2: = B2
D2: = A2
Is this what you're looking to do?
A simple formula could be written that 'floats' the range totals for each successive load ID.
In the following, I've typed 25 and 15 in D1:E1 and used a custom number format of I\D 0. In this way, the column is identified and the cell can be referenced as a true number load limit. You can hard-code the limits into the formula if you prefer by overwriting D$1 but you will not have a one-size-fits-all formula that can be copied right for alternate load limits as I have in my example..
The formula in D2 is,
=IF(ROW()=2, 1, (SUM(INDEX($B:$B, MATCH(D1, D1:D$1, 0)):$B2)>D$1)+ D1)
Fill right to E2 then down as necessary.
I have 1 Virtual cube consists of 2 cubes.
Example of fact table of 1st cube.
id object_id time_id date_id state
1 10 2 1 0
2 11 5 1 0
3 10 7 1 1
4 10 3 1 0
5 11 4 1 0
6 11 7 1 1
7 10 8 1 0
8 11 5 1 0
9 10 7 1 1
10 10 9 1 2
Where State: 0 - Ok, 1 - Down, 2 - Unknown
For this cube I have one measure StateCount it should count States for each object_id.
Here for example we have such result:
for 10 : 3 times Ok , 2 times Down, 1 time Unknown
for 11 : 3 times Ok , 1 time Down
Second cube looks like this:
id object_id time_id date_id status
1 10 2 1 0
2 11 5 1 0
3 10 7 1 1
4 10 3 1 1
5 11 4 1 1
Where Status: 0 - out, 1 - in. I keep this in StatusDim.
In this table I keep records that should not be count. If object have status 1 that means that I have exclude it from count.
If we intersect these tables and use StateCount we will receive this result:
for 10 : 2 times Ok , 1 times Down, 1 time Unknown
for 11 : 2 times Ok , 1 time Down
As far as i know, i must use calculated member with IIF condition. Currently I'm trying something like this.
WITH MEMBER [Measures].[StateTimeCountDown] AS(
iif(
[StatusDimDown.DowntimeHierarchy].[DowntimeStatus].CurrentMember.MemberValue
<> "in"
, [Measures].[StateTimeCount]
, null )
)
The multidimensional way to do this would be to make attributes from your state and status columns (hopefully with user understandable members, i. e. using "Ok" and not "0"). Then, you can just use a normal count measure on the fact tables, and slice by these attributes. No need for complex calculation definitions.