Given a table with data like:
A
B
Qty.
Running Total
5
5
5
10
5
15
I can create the running total using the formula =SUM($A$2:A2) and then drag down to get the running total after each quantity (here Qty.)
What may I do for calculating running total using two columns which may or may not be consecutive as shown below:
A
B
C
D
Qty. 1
Other
Qty. 2
RT
2
blah
2
4
2
phew
2
8
3
xyz
2
13
Place in cell D2 the formula =SUM(A2,C2,D1). Do not pay attention to the fact that the function will refer to a non-numeric cell D1 - the SUM() function will not break, unlike ordinary addition =A2+C2+D1. Now, just stretch the formula down.
Related
I have a SQL table which has two columns called seq and sub_seq as seen below. I would like to add a third column called id, which goes up by 1 every time the sub_seq starts again at 1 as shown in the table below.
seq
sub_seq
id
1
1
1
2
2
1
3
3
1
4
4
1
5
5
1
6
1
2
7
2
2
8
3
2
9
1
3
10
2
3
11
3
3
12
4
3
13
5
3
14
6
3
15
7
3
I could write a solution using plpgsql, however I would like to know if there is a way of doing this in standard SQL. Any help would be greatly appreciated.
If sub_seq is always a running sequence then you can use the DENSE RANK function and order over the differences of two columns, assuming it will consistently uniform.
SELECT seq, sub_Seq, DENSE_RANK() OVER (ORDER BY seq-sub_Seq) AS id
FROM tableDemo
This solution is based on the sample data you have provided, I think more sample data would be helpful to check the whole scenario.
To be generic the issue is: I need to create group means that exclude own group observations before calculating the mean.
As an example: let's say I have firms, products and product characteristics. Each firm (f=1,...,F) produces several products (i=1,...,I). I would like to create a group mean for a certain characteristic of the product i of firm f, using all products of all firms, excluding firm f product observations.
So I could have a dataset like this:
firm prod width
1 1 30
1 2 10
1 3 20
2 1 25
2 2 15
2 4 40
3 2 10
3 4 35
To reproduce the table:
firm=[1,1,1,2,2,2,3,3]
prod=[1,2,3,1,2,4,2,4]
hp=[30,10,20,25,15,40,10,35]
x=[firm' prod' hp']
Then I want to estimate a mean which will use values of all products of all other firms, that is excluding all firm 1 products. In this case, my grouping is at the firm level. (This mean is to be used as an instrumental variable for the width of all products in firm 1.)
So, the mean that I should find is: (25+15+40+10+35)/5=25
Then repeat the process for other firms.
firm prod width mean_desired
1 1 30 25
1 2 10 25
1 3 20 25
2 1 25
2 2 15
2 4 40
3 2 10
3 4 35
I guess my biggest difficulty is to exclude the own firm values.
This question is related to this page here: Calculating group mean/medians in MATLAB where group ID is in a separate column. But here, we do not exclude the own group.
p.s.: just out of curiosity if anyone works in economics, I am actually trying to construct Hausman or BLP instruments.
Here's a way that avoids loops, but may be memory-expensive. Let x denote your three-column data matrix.
m = bsxfun(#ne, x(:,1).', unique(x(:,1))); % or m = ~sparse(x(:,1), 1:size(x,1), true);
result = m*x(:,3);
result = result./sum(m,2);
This creates a zero-one matrix m such that each row of m multiplied by the width column of x (second line of code) gives the sum of other groups. m is built by comparing each entry in the firm column of x with the unique values of that column (first line). Then, dividing by the respective count of other groups (third line) gives the desired result.
If you need the results repeated as per the original firm column, use result(x(:,1))
I am new to tableau and need help in figuring this out.I have a dataset in below format:
hid:id for the house the customer belong
cid:customer id
hID CustomerID
1 A
1 B
1 C
2 D
2 E
3 F
3 G
3 H
3 I
4 J
5 K
5 L
5 M
5 N
5 O
So A,B belong to house 1 so count of hid '1' is 3 so:
hid count of members
1 3
2 2
3 3
4 1
5 5
I want to show a graph in tableau as size of house that is X-axis :Size of house and Y-axis :Count no of house with same size so for above data the values as below:
Size of house no of house
1 1
2 1
3 2
4 0
5 1
The final graph should be:
In Tableau jargon, you're looking to bin based upon an aggregate value. Take a look at the following blog post for a more detailed description/walk-through.
One way to accomplish this is by leveraging talbeau's level of detail calculations. Creating a calculated field along the lines of:
{FIXED [hID] : COUNTD([CustomerID])}
You can then create a bin field by right clicking on the new field and binning based on a parameter, or a a static size (1?) of your choosing.
To create the visual, place this second bin field on the row shelf and on the column shelf drag the hID dimension and right click to convert to a measure by selecting Count Distinct.
As a side note, depending on whether you set your bin field as continuous or discrete, the 4 bin in your sample data will or will not appear.
I am fairly new to using MATLAB. I have two text files that have 500 rows x 100 columns and every one of it has a random value.
For example:
1 2 3 4 5
6 7 8 9 2
4 5 6 6 8
So my question is, corresponding to the row and column, how can I calculate the average of each particular field corresponding to the row and column?
For instance, at (1,1) in text1.txt is 20 and at (1,1) in text2.txt is 40. The average is 30 and I want to display this result in an another file at (1,1).
Many thanks.
assuming the files are named 'a' and 'b':
load a
load b
c=(a+b)/2;
save('c','c','-ascii');
I have a series of times and returns in various matrices lets call them a b c. They are all x by 2 with column 1 being times in seconds and column 2 returns. While all the returns are over a set series of time intervals like 15s 30s 45s etc the problem is not all the matrices have all the time buckets so while a might be a 30 by 2 , b might only be a 28 by 2. Because it is missing say time 45 seconds and a return. I want to go through each matrix and where I am missing a time bucket I want to insert the bucket with a zero return - I am happy to create a control 30 by 1 matrix with all the times that need to be cross referenced
You can use ismember to locate these missing positions, so if a is the control vector and b is the missing data vector ind=find(ismember(a,b)==0); will give you the indices of a that are missing in b.
For example:
a=1:10;
b=[1:2 4:5 7:10];
ind=find(ismember(a,b)==0);
ind =
3 6
In order to to add zeros in the right places for b just
for n=1:numel(ind)
b=[b(1:ind(n)-1) , 0 , b(ind(n):end)];
end
b =
1 2 0 4 5 0 7 8 9 10