I have to put together a report every quarter using data pulled off of Morningstar Direct. I have to automate the whole process, or at least parts of it. We have put this report together for the last two quarters, and we use the same format each time. So, we already have the general templates for the report - now I'm just looking for a way to pull the data from Morningstar and putting into the templates correctly.
Does anyone have any general idea where I should start?
A B C D E F
Group Name Weight Gross Net Contribution
Equity 25% 10% 8% .25
IBM 5% 15% 12%
AAPL 7% 23% 18%
Fixed Income 25% 5% 4% .17
10 Yr Bond 10% 7% 5%
Emerging Mrkts
And it goes on breaking things into more groups, and there are many more holdings within each group.
What I want it to do is search until it finds "Equity", for example, and then go over one row, grab the name of the position, its weight, and its net return, and do that for each holding in Equity. The for it to do the same thing in Fixed Income, and on and on - selecting the names, weights, and nets for each holding. Then copy and pasting them into another workbook.
Anyway that is possible?
It sounds like you need to parse your information. By using left(), right(), and mid() you can select the good data and ignore the superfluous. You could separate the data in one cell into multiple cells in the desired format.
A B
Name Address
John Q. Public 123 My Street, City, State, Zip
E (First Name) F (Middle Initial) (extra work to program missing data)
=LEFT(A2,FIND(" ",A2)) =MID(A2,LEN(E2)+1,FIND(" ",MID(A2,LEN(E2)-1,99)))
G (Last Name) H (City)
=MID(A2,(LEN(E2)+LEN(F2)+2),99) =MID(B2,LEN(H2)+2,FIND(",",MID(B2,LEN(H2)+2,99))-1)
I (State)
=MID(B2,(LEN(I2)+LEN(H2)+4),FIND(",",MID(B2,(LEN(I2)+LEN(H2)+4),99))-1)
J (Zip Code)
=MID(B2,(LEN(H2)+LEN(I2)+LEN(J2)+6),99)
This code will parse the name in the cell A2 and address in cell B2 into separate fields.
Similar cuts should allow you to get rid of the unwanted data.
==================================================================
7/8/2015
Your data seems to be your desired output. If so, please provide sanitized input data for comparison. You probably need to loop through your input to find the groups. When the group changes, prepare the summary figures.
Related
I have a dataset. I am using pandas dataframe and named it df.
The dataset has 50,000 rows - here are the first 5:.
Name_Restaurant cuisines_available Average cost
Food Heart Japnese, chinese 60$
Spice n Hungary Indian, American, mexican 42$
kfc, Lukestreet Thai, Japnese 29$
Brown bread shop American 11$
kfc, Hypert mall Thai, Japnese 40$
I want to create column which contains the no. of cuisines available
I am trying code
df['no._of_cuisines_available']=df['cuisines_available'].str.len()
Then instead of showing the no. of cuisines, it is showing the sum of charecters.
For example - for first row the o/p should be 2 , but its showing 17.
I need a new column that contain number of stores for each restaurant. example -
here kfc has 2 stores kfc, lukestreet and kfc, hypert mall. I have completely
no idea how to code this.
i)
df['cuisines_available'].str.split(',').apply(len)
ii)
df['Name_Restaurant'].str.split(',', expand=True).melt().['value'].str.strip().value_counts()
What ii) does: split columns at ',' and store all strings thus generated in an individual column. Then use melt to make one big column, strip away spaces etc. and count individual entries.
my major goal is to calculate Conditional Probability over a large number of rows. Hence the use of Powerpivot.
Attached is an excel file with 10 rows as an example of how I did it in Excel.
My challenge is the formula in column F which I will then be needing to calculate column G.
Tamir
Can you check the solution
Main Formulas:
Calculate a Total, without filtering BRAND and UPS (calculated measure)
=CALCULATE([Total],All(Brand),All(upc))
Sum IF UPC (calculated column):
=CALCULATE([Total],filter(ALL(Fact),Fact[UPC]= EARLIER(Fact[UPC]) ))
I have a google spreadsheet for my gaming information. It contains 2 sheets - one for monster information, another for team.
Monster information sheet contains the attack value, defend value, and the mana cost of monsters. It's almost like a database of monsters that I can summon.
Team sheet does the following:
Asks for the amount of mana I currently have.
Computes a list of up to 5 monsters that I can summon (it can be less than 5).
Each monster has their own mana cost, therefore total mana cost mustn't exceed the amount of mana I have given in point 1.
The tabulated list should give me a team that have the highest combined attack value. It does not matter how many monsters are summoned. Each monster cannot be summoned twice though.
I have been thinking of using query() function so that I can make use of SQL statements. (so that I can hopefully retrieve the tabulated list directly)
Sample: Monster Info
A B C D
1 Monster Attack Defense Cost
2 MonA 1200 1200 35
3 MonB 1400 1300 50
... ...
Sample: Team
A B C D
1 Mana 120
2
3 Attack Team
4 Monster Attack Cost Total Attack
5 MonB 1400 50 1400
6 MonA 1200 35 2600
7 ... ...
I have these formula in "Team" sheet
A5: =query('Monster Info'!$A$:$D,"SELECT A,B,D ORDER BY B DESC LIMIT 5")
B5: =CONTINUE(A5, 1, 2)
C5: =CONTINUE(A5, 1, 3)
D5: =C5
A6: =CONTINUE(A5, 2, 1)
B6: =CONTINUE(A5, 2, 2)
C6: =CONTINUE(A5, 2, 3)
D6: =D5+C6
That only gets the 5 best attack monsters, regardless of the mana cost consideration. How do I do that such that it takes consideration of both attack value and mana cost value? There is another problem shown in the example below:
Example: (simplified version, without defense value etc)
Monster Attack Cost
MonA 1400 50
MonB 1200 35
MonC 1100 30
MonD 900 25
MonE 500 20
MonF 400 15
MonG 350 10
MonH 250 5
If I have 160 mana, then the obvious team is A+B+C+D+E (5100 Attack).
If I have 150 mana, it becomes A+B+C+D+G (4950 Attack).
If I have 140 mana, it becomes A+B+C+D (4600 Attack).
If I have 130 mana, it becomes B+C+D+E+F (4100 Attack using 125 mana) or A+B+C+F (4100 Attack using all 130 mana).
If I have 120 mana, it becomes B+C+D+E+G (4050 Attack).
If I have 110 mana, it becomes B+C+D+F+H (3850 Attack).
As you can see, there isn't really a pattern within the results.
Any expert willing to share their insights on this?
I've played with the problem for an hour and I only have a workaround here. Your problem seems to be a standard linear programming task which should can easily be solved by a "Solver" software. There used to be a so called "Solver" in google spreadsheet, but unfortunately it was removed from the newest version. If you are not insisting on Google solution, you should try it in one of the Solver-supported spreadsheet manager softwares.
I tried MS Office (it has a Solver add-in, installation guide: http://office.microsoft.com/en-001/excel-help/load-the-solver-add-in-HP010342660.aspx).
Before you run the solver, you should prepare your original dataset a bit, with helper columns and cells.
Add a new column next to the "Cost" column (let's assume it is column "D"), and under it put each row either 0, or 1. This column will tell you if a monster is selected to the attack team or not.
Add two more columns ("E" and "F" respectively). These columns will be products of the Attack and of the Cost respectively. So you should write a function to the E2 cell: =b2*d2, and for the F2 cell: =c2*d2. With this way if a monster is selected (which is told by the D column, remember), the appropriate E and F cells will be non zero values, aotherwise they will be 0.
Create a SUM row under the last row, and create a summarizing function for the D,E,F columns respectively. So in my spreadsheet D10 cell gets its value like this: =sum(d2:d9), and so on.
I created a spreadsheet to show these steps: https://docs.google.com/spreadsheets/d/1_7XRlupEEwat3CthSSz8h_yJ44MysK9hMsj0ijPEn18/edit?usp=sharing
Remember to copy this worksheet to an MS Office worksheet, before you start the Solver.
Now, you are ready to start the Solver. (Data menu, Solver in MS Office). You can see a video here on using the Solver: https://www.youtube.com/watch?v=Oyc0k9kiD7o
It's not that hard as it looks like, but for this case I'll describe what to write where:
Set Objective: you should select the "E10" cell, as that represents the sum of all the attack points.
Check "Max" radiobutton as we would like to maximize the value of the attacks.
By Changing variable cells: Select the "d2:d9" interval as those cells are representing whether a monster is selected or not. The solver will try to adjust these values (0, or 1) in order to maximise the sum attack.
Subject to the Contraints: Here we should add some constraints. Click on the Add button, and then:
First we should ensure that d2:d9 are all binary values. So "Cell reference" should be "d2:d9" and from the dropdown menu, select "bin" as binary.
Another constraint should be that the sum of the selected monsters should not exceed 5. So select the cell where the sum of the selected monsters is represented (D10) and add "<=" and the value "5"
Finally we cannot use more manna that we have, so select the cell in which you store the sum of used manna (F2), and "<=", and add the whole amount of manna we can spend in my case it's in the I2 cell).
Done. It should work, in my case it worked at least.
Hope it helps anyway.
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.