I've been learning TSQL and need some help with a conversion CPU MIPS into PERCENTAGE.
I've built my code to get some data that I'm expecting. In addition to this, I want to add a column to my code which is to get the CPU%. I have a column that gives me TOTALCPU MIPS and want to use this in the code but in the form of percentage. Example, I have these values in my TOTAL CPU Column:
1623453.66897
0
0
2148441.01573933
3048946.946314
I want to convert these values into percentage and use them. I couldn't find much info on the internet.
Appreciate your response.
I assume that you have 5 numeric quantities (2 of them being zero) and you want to find the percentage that corresponds to each of them out of the addition of the five quantities. Is it so?
To find the percentage of a particular number in the addition you multiply the number by 100 and divide by the addition, the result is the percentage that that number is in relation with the addition.
The sum: 6820841.631023
The percentage of the first number (of MIPS):
1623453.668970 * 100 / 6820841.631023 = 23.80136876 =>
23.80136876% is the percentage of CPU used by the first program.
To give the answer some SQL looking, refering to Mips_Table as the view/table that contains the MIPs data:
select mips, mips/totMips*100 Pct_CPU
from Mips_Table,
(select sum(mips) TotMips from Mips_Table) k
I'm trying to create a matching algo in pandas that does the following with a given table:
A table contains purchases and sales of products by date, item, quantity (+ for purchases and - for sales) and price.
Conditions:
Create an algorithm that matches purchases and sales per item and the corresponding average profit for each item in total.
Matches can only be on the same date, otherwise they are not matched at all.
Remaining positive or negative inventories per day are ignored
Negative inventories are allowed.
Example with a single product:
date product quantity price
1 X +2 1
1 X -1 2
1 X -2 4
2 X +1 1
2 X +1 2
3 X -1 4
Answer:
The result would be that only on day 1 the 3 trades are matched, with a profit of -2+2+4=4. Because inventory is +2, -1, and then again -1. The remaining inventory of -1 is ignored. Day 2 and 3 have no matches because the trades are not closed on the same day.
Correct output:
product Profit
X +4
Is there any elegant way to get to this result without having to loop over the table multiple times with iterrow?
For reproducing the df:
df = pd.DataFrame({'date':[1,1,1,2,2,3],'product': ['X']*6,'quantity':[2,-1,-2,1,1,-1],'price':[1,2,4,1,2,4]})
The process that you describing could use groupby & aggregate, something like this:
df.groupby('date').sum()
But I don't fully understand your rules for matching. So in Day 1, I got a different total profit. Price * quantity is (+2*1)+(-1*2)+(-2*4)=-8, so profit seems to be 8.
Using iterrow() is a rather bad practice. Not only you're writing excessive code, but also it's likely much slower (check a comparison here).
Most of those type of jobs can be accomplished by combining groupby(), aggregate() and apply(). Check out this great tutorial.
I hope this helps you or future answers :)
I have a capex table to calculate investment of a new product.
As shown in pic all numbers are not real numbers
D83 is the initial investment, meaning we invest $15,800 to buy machine and space
E85 to I85 are the variable cost each year.
E86 to I86 are the revenue each year, revenue = price * est.sold quantity
E87=E85+E86, F87=F85+F86, etc.
D91 = {INDEX($E$87:$I$87,MATCH(TRUE,$E$89:$I$89>0,0))} This is an array to find the first positive cash flow of Row 89 and return the value in row 87
D92 = {INDEX($E$83:$I$83,MATCH(TRUE,$E$89:$I$89>0,0))} This is an array to find the first positive cash flow of Row 89 and return the value in row 83
D93 = COUNTIF(E89:I89,"<0")+((-D92)/D91)
In this way, I calculated the payback years of a project.
In cell D98 I need to calculate that given a certain payback year number, other things hold still, what the price needs to be. Since D91 and D92 are arrays to lookup for the first positive cash flow and each year cash flow is not even, I can't think of a formula to put in D98 so D98 changes with C98, for example, if I put 2 in C98 meaning I need the payback period to be 2 years, and D98 will automatically give me a number to show what price it should be.
Is there any formula or VBA to do it?
First up, my environment: SQL 2005 + MS DAX 2009.
We have made a table that gets used in a matrix-like fashion for entering in purchase orders via an AX form. So each row will have:
a column for item#
a column for color
columns 1-7 for size (size1, size2,...), quantity (qty1, qty2,...), and cost (cost1, cost2,...).
I am trying to create a report in SSRS that basically uses this data in a more list-like fashion for printing out a PO order form.
I have got it to show the sizing right, but the cost situation complicates it as the unit cost can, and does, differ depending on size (for instance 2XL is more than S-M-L).
For example in our table, item 10000 black has 3 for Small (this data would be qty1), 3 for Medium (qty2) and 4 2XL (qty5). The cost for qty1 and qty2 are the same at $2.50 (cost1 and cost2). The cost for qty5 (cost5) would be $4. I would like to have this broken out into 2 rows by the cost and associated size on the form. So one line would have 10000 black Small and medium info and the second row would have the same item and color, but only have 2XL and its cost data.
Is there a way to "match" fields or somehow cycle through them to get the correct cost without having to have an additional 7 cost columns? Or perhaps there is a more elegant solution that is escaping me?
I basically need the answer to this SO question that provides a power-law distribution, translated to T-SQL for me.
I want to pull a last name, one at a time, from a census provided table of names. I want to get roughly the same distribution as occurs in the population. The table has 88,799 names ranked by frequency. "Smith" is rank 1 with 1.006% frequency, "Alderink" is rank 88,799 with frequency of 1.7 x 10^-6. "Sanders" is rank 75 with a frequency of 0.100%.
The curve doesn't have to fit precisely at all. Just give me about 1% "Smith" and about 1 in a million "Alderink"
Here's what I have so far.
SELECT [LastName]
FROM [LastNames] as LN
WHERE LN.[Rank] = ROUND(88799 * RAND(), 0)
But this of course yields a uniform distribution.
I promise I'll still be trying to figure this out myself by the time a smarter person responds.
Why settle for the power-law distribution when you can draw from the actual distribution ?
I suggest you alter the LastNames table to include a numeric column which would contain a numeric value representing the actual number of indivuduals with a name that is more common. You'll probably want a number on a smaller but proportional scale, say, maybe 10,000 for each percent of representation.
The list would then look something like:
(other than the 3 names mentioned in the question, I'm guessing about White, Johnson et al)
Smith 0
White 10,060
Johnson 19,123
Williams 28,456
...
Sanders 200,987
..
Alderink 999,997
And the name selection would be
SELECT TOP 1 [LastName]
FROM [LastNames] as LN
WHERE LN.[number_described_above] < ROUND(100000 * RAND(), 0)
ORDER BY [number_described_above] DESC
That's picking the first name which number does not exceed the [uniform distribution] random number. Note how the query, uses less than and ordering in desc-ending order; this will guaranty that the very first entry (Smith) gets picked. The alternative would be to start the series with Smith at 10,060 rather than zero and to discard the random draws smaller than this value.
Aside from the matter of boundary management (starting at zero rather than 10,060) mentioned above, this solution, along with the two other responses so far, are the same as the one suggested in dmckee's answer to the question referenced in this question. Essentially the idea is to use the CDF (Cumulative Distribution function).
Edit:
If you insist on using a mathematical function rather than the actual distribution, the following should provide a power law function which would somehow convey the "long tail" shape of the real distribution. You may wan to tweak the #PwrCoef value (which BTW needn't be a integer), essentially the bigger the coeficient, the more skewed to the beginning of the list the function is.
DECLARE #PwrCoef INT
SET #PwrCoef = 2
SELECT 88799 - ROUND(POWER(POWER(88799.0, #PwrCoef) * RAND(), 1.0/#PwrCoef), 0)
Notes:
- the extra ".0" in the function above are important to force SQL to perform float operations rather than integer operations.
- the reason why we subtract the power calculation from 88799 is that the calculation's distribution is such that the closer a number is closer to the end of our scale, the more likely it is to be drawn. The List of family names being sorted in the reverse order (most likely names first), we need this substraction.
Assuming a power of, say, 3 the query would then look something like
SELECT [LastName]
FROM [LastNames] as LN
WHERE LN.[Rank]
= 88799 - ROUND(POWER(POWER(88799.0, 3) * RAND(), 1.0/3), 0)
Which is the query from the question except for the last line.
Re-Edit:
In looking at the actual distribution, as apparent in the Census data, the curve is extremely steep and would require a very big power coefficient, which in turn would cause overflows and/or extreme rounding errors in the naive formula shown above.
A more sensible approach may be to operate in several tiers i.e. to perform an equal number of draws in each of the, say, three thirds (or four quarters or...) of the cumulative distribution; within each of these parts list, we would draw using a power law function, possibly with the same coeficient, but with different ranges.
For example
Assuming thirds, the list divides as follow:
First third = 425 names, from Smith to Alvarado
Second third = 6,277 names, from to Gainer
Last third = 82,097 names, from Frisby to the end
If we were to need, say, 1,000 names, we'd draw 334 from the top third of the list, 333 from the second third and 333 from the last third.
For each of the thirds we'd use a similar formula, maybe with a bigger power coeficient for the first third (were were are really interested in favoring the earlier names in the list, and also where the relative frequencies are more statistically relevant). The three selection queries could look like the following:
-- Random Drawing of a single Name in top third
-- Power Coef = 12
SELECT [LastName]
FROM [LastNames] as LN
WHERE LN.[Rank]
= 425 - ROUND(POWER(POWER(425.0, 12) * RAND(), 1.0/12), 0)
-- Second third; Power Coef = 7
...
WHERE LN.[Rank]
= (425 + 6277) - ROUND(POWER(POWER(6277.0, 7) * RAND(), 1.0/7), 0)
-- Bottom third; Power Coef = 4
...
WHERE LN.[Rank]
= (425 + 6277 + 82097) - ROUND(POWER(POWER(82097.0, 4) * RAND(), 1.0/4), 0)
Instead of storing the pdf as rank, store the CDF (the sum of all frequencies until that name, starting from Aldekirk).
Then modify your select to retrieve the first LN with rank greater than your formula result.
I read the question as "I need to get a stream of names which will mirror the frequency of last names from the 1990 US Census"
I might have read the question a bit differently than the other suggestions and although an answer has been accepted, and a very through answer it is, I will contribute my experience with the Census last names.
I had downloaded the same data from the 1990 census. My goal was to produce a large number of names to be submitted for search testing during performance testing of a medical record app. I inserted the last names and the percentage of frequency into a table. I added a column and filled it with a integer which was the product of the "total names required * frequency". The frequency data from the census did not add up to exactly 100% so my total number of names was also a bit short of the requirement. I was able to correct the number by selecting random names from the list and increasing their count until I had exactly the required number, the randomly added count never ammounted to more than .05% of the total of 10 million.
I generated 10 million random numbers in the range of 1 to 88799. With each random number I would pick that name from the list and decrement the counter for that name. My approach was to simulate dealing a deck of cards except my deck had many more distinct cards and a varing number of each card.
Do you store the actual frequencies with the ranks?
Converting the algebra from that accepted answer to MySQL is no bother, if you know what values to use for n. y would be what you currently have ROUND(88799 * RAND(), 0) and x0,x1 = 1,88799 I think, though I might misunderstand it. The only non-standard maths operator involved from a T-SQL perspective is ^ which is just POWER(x,y) == x^y.