Pivot tables are very powerful analysis tools. They can summarize vast amounts of data with just few clicks. But they are lousy when it comes to output. Imagine the horror of putting a pivot table right inside your beautiful dashboard. One refresh could ruin the layout and create half-an-hour extra work for you.
How to combine the power of pivot tables with elegance of your dashboards?
The answer is: GETPIVOTDATA()
What is GETPIVOTDATA?
As the name suggests, GETPIVOTDATA gets pivot table data. The best way to understand GETPIVOTDATA is by looking at an example.
Let’s say, you have a pivot table like the one below. And you want to know what is the Amount for Cust Area = Middle & Prod Category = Biscuits combination.
The below GETPIVOTDATA formula should work.
=GETPIVOTDATA(“Amount”,$A$3,”Cust Area”,”Middle“,”Prod Category”,”Biscuits“)
As you can see GETPIVOTDATA has below syntax.
GETPIVOTDATA(value field name, any cell reference in pivot table, [field name 1, value1, field name 2, value 2 …])
Few more examples of GETPIVOTDATA:
Check out below examples to understand how various parameters of the GETPIVOTDATA function behave:
| GETPIVOTDATA function | What it does | Value |
|---|---|---|
| =GETPIVOTDATA(“Amount”,$A$3,”Cust Area”,”South”,”Prod Category”,”Biscuits”) | Gets Amount for South & Biscuits combination | $609.50 |
| =GETPIVOTDATA(“Amount”,$A$3,”Prod Category”,”Biscuits”) | Gets grand total for Biscuits | $5,251.10 |
| =GETPIVOTDATA(“Amount”,$A$3,”Cust Area”,”South”) | Gets grand total for South | $4,342.20 |
| =GETPIVOTDATA(“Amount”,$A$3) | Gets grand total of all amounts | $41,828.00 |
| =GETPIVOTDATA(“Amount”,$A$1,”Cust Area”,”West”,”Prod Category”,”Snacks”) | Gives an error as $A$1 is not part of the pivot | #REF! |
| =GETPIVOTDATA(“Amount”,$A$3,”Cust Area”,$P$2,”Prod Category”,$P$3) | Gets Amount for cust area = P2 and pro category = P3 cell values. | depends on variables |
| =GETPIVOTDATA(“Amount”,$A$3,”Prod Category”,category_name) | Gets grand total for category = category_name value | depends on variables |
| =GETPIVOTDATA($P$4&””,$A$3,”Cust Area”,$P$2,”Prod Category”,$P$3) | Gets P4 value field for Cust Area = P2 and Prod Category = P3. Note: $P$4 &”” is used to convince GPD that P4 is a string not number. |
depends on variables |
Using GETPIVOTDATA in dashboards
The idea is simple. Since GETPIVOTDATA can be parameterized with variable cells or named ranges, we can use it in dashboards to get relevant data based on user input.
Sample this:
Or this dashboard powered with GETPIVOTDATA
Things to note when working with GETPIVOTDATA:
GETPIVOTDATA is a very useful function, but it does have a few quirks.
- If your pivot table has slicers linked to them, GPD will reflect the results based on slicer selection.
- If your pivot table has any items filtered (say category Biscuits is filtered out), then GPD will return #REF error when you try to get any value corresponding to Biscuits.
- If you change the pivot table structure, your GETPIVOTDATA functions may not work as you expect.
- If you turn off grand totals or sub-totals, you can no longer get them thru GPD.
- GPD requires that your original pivot tables remain intact and visible all the time.
- If you want to completely get rid of pivot tables and still get answers to questions, then you should use CUBE formulas along with Workbook data model feature (more on this in a future post).
- The best & easiest way to write GPD is by pressing = and referencing a cell inside the pivot. This will automatically write the GPD for you. You can then customize the parameters as you need.
- You can turn-off GPD by going to Pivot Table Analyze ribbon tab & unchecking “Generating GETPIVODATA” option from PivotTable options area.
Download GETPIVOTDATA Examples workbook
Please click here to download the GETPIVOTDATA example workbook. Refer to various tabs & formulas to learn more. Don’t forget to play with the dashboard powered by GETPIVOTDATA.
Learn more about Pivot Tables
If you are new to Pivot Tables, it’s high time you started using them. Check out below pages and get started.
- Introduction to Excel Pivot Tables – article , Podcast
- Comprehensive guide to Excel Pivot Tables
- Slicers – Introduction, what are they, advanced scenarios
- Building dashboards with Pivot Tables + Slicers
- Convert regular pivots to GETPIVOTDATA – 3 part tutorial from Mike Alexander, part 2 & part 3
How do you use GETPIVOTDATA?
Let me be honest. For my dashboards, I usually write direct cell references (=A7) instead of GPD. This keeps my formulas short. For dynamic / parameterized setups, I usually write INDEX / MATCH formulas that talk to Pivot Table data. But occasionally I use GETPIVOTDATA because it is very easy to setup and does what it says on the sticker.
What about you? How do you use GETPIVOTDATA? Please share scenarios in the comments section.
20 Responses to “Simulating Dice throws – the correct way to do it in excel”
You have an interesting point, but the bell curve theory is nonsense. Certainly it is not what you would want, even if it were true.
Alpha Bravo - Although not a distribution curve in the strict sense, is does reflect the actual results of throwing two physical dice.
And reflects the following . .
There is 1 way of throwing a total of 2
There are 2 ways of throwing a total of 3
There are 3 ways of throwing a total of 4
There are 4 ways of throwing a total of 5
There are 5 ways of throwing a total of 6
There are 6 ways of throwing a total of 7
There are 5 ways of throwing a total of 8
There are 4 ways of throwing a total of 9
There are 3 ways of throwing a total of 10
There are 2 ways of throwing a total of 11
There is 1 way of throwing a total of 12
@alpha bravo ... welcome... 🙂
either your comment or your dice is loaded 😉
I am afraid the distribution shown in the right graph is what you get when you throw a pair of dice in real world. As Karl already explained, it is not random behavior you see when you try to combine 2 random events (individual dice throws), but more of order due to how things work.
@Karl, thanks 🙂
When simulating a coin toss, the ROUND function you used is appropriate. However, your die simulation formula should use INT instead of ROUND:
=INT(RAND()*6)+1
Otherwise, the rounding causes half of each number's predictions to be applied to the next higher number. Also, you'd get a count for 7, which isn't possible in a die.
To illustrate, I set up 1200 trials of each formula in a worksheet and counted the results. The image here shows the table and a histogram of results:
http://peltiertech.com/WordPress/wp-content/img200808/RandonDieTrials.png
@Jon: thanks for pointing this out. You are absolutely right. INT() is what I should I have used instead of ROUND() as it reduces the possibility of having either 1 or 6 by almost half that of having other numbers.
this is such a good thing to learn, helps me a lot in my future simulations.
Btw, the actual graphs I have shown were plotted based on randbetween() and not from rand()*6, so they still hold good.
Updating the post to include your comments as it helps everyone to know this.
By the way, the distribution is not a Gaussian distribution, as Karl points out. However, when you add the simulations of many dice together (i.e., ten throws), the overall results will approximate a Gaussian distribution. If my feeble memory serves me, this is the Central Limit Theorem.
@Jon, that is right, you have to nearly throw infinite number of dice and add their face counts to get a perfect bell curve or Gaussian distribution, but as the central limit theorem suggests, our curve should roughly look like a bell curve... 🙂
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I'm afraid to say that this is a badly stated and ambiguous post, which is likely to cause errors and misunderstanding.
Aside from the initial use of round() instead of int(),.. (you've since corrected), you made several crucial mistakes by not accurately and unambiguously stating the details.
Firstly, you said:
"this little function generates a random fraction between 0 and 1"
Correctly stated this should be:
"this little function generates a random fraction F where 0 <= F < 1".
Secondly, I guess because you were a little fuzzy about the exact range of values returned by rand(), you have then been just as ambiguous in stating:
"I usually write int(rand()*12)+1 if I need a random number between 0 to 12".
(that implies 13 integers, not 12)
Your formula, does not return 13 integers between 0 to 12.
It returns 12 integers between 1 and 12 (inclusive).
-- As rand() returns a random fraction F where 0 <= F < 1, you can obviously can only get integers between 1 and 12 (inclusive) from your formula as stated above, but clearly not zero.
If you had said either:
"I usually write int(rand()*12) if I need a random number between 0 to 11 (inclusive)",
or:
"I usually write int(rand()*12)+1 if I need a random number between 1 to 12 (inclusive)"
then you would have been correct.
Unfortunately, you FAIL! -- repeat 5th grade please!
Your Fifth Grade Maths Teacher
Idk if I'm on the right forum for this or how soon one can reply, but I'm working on a test using Excel and I have a table set up to get all my answers from BUT I need to generate 10,000 answers from this one table. Every time, I try to do this I get 10,000 duplicate answers. I know there has to be some simple command I have left out or not used at all, any help would be extremely helpful! (And I already have the dice figured out lol)
Roll 4Dice with 20Sides (4D20) if the total < 20 add the sum of a rerolled 2D20. What is the average total over 10,000 turns? (Short and sweet)
Like I said when I try to simulate 10,000turns I just get "67" 10,000times -_- help please! 😀
@Justin
This is a good example to use for basic simulation
have a look at the file I have posted at:
https://rapidshare.com/files/1257689536/4_Dice.xlsx
It uses a variable size dice which you set
Has 4 Dice
Throws them 10,000 times
If Total per roll < 20 uses the sum of 2 extra dice Adds up the scores Averages the results You can read more about how it was constructed by reading this post: http://chandoo.org/wp/2010/05/06/data-tables-monte-carlo-simulations-in-excel-a-comprehensive-guide/
Oh derp, i fell for this trap too, thinking i was makeing a good dice roll simulation.. instead of just got an average of everything 😛
Noteably This dice trow simulate page is kinda important, as most roleplay dice games were hard.. i mean, a crit failure or crit hit (rolling double 1's or double 6's) in a a game for example dungeons and dragons, if you dont do the roll each induvidual dice, then theres a higher chance of scoreing a crit hit or a crit failure on attacking..
I've been working on this for awhile. So here's a few issues I've come across and solved.
#1. round() does work, but you add 0.5 as the constant, not 1.
trunc() and int() give you the same distributions as round() when you use the constant 1, so among the three functions they are all equally fair as long as you remember what you're doing when you use one rather than the other. I've proven it with a rough mathematical proof -- I say rough only because I'm not a proper mathematician.
In short, depending on the function (s is the number of sides, and R stands in for RAND() ):
round(f), where f = sR + 0.5
trunc(f), where f = sR + 1
int(f), where f = sR + 1
will all give you the same distribution, meaning that between the three functions they are fair and none favors something more than the others. However...
#2. None of the above gets you around the uneven distribution of possible outcomes of primes not found in the factorization of the base being used (base-10, since we're using decimal; and the prime factorization of 10 is 2 and 5).
With a 10-sided die, where your equation would be
=ROUND(6*RAND()+0.5)
Your distribution of possible values is even across all ten possibilities.
However, if you use the most basic die, a 6-sided die, the distributions favor some rolls over others. Let's assume your random number can only generate down to the thousandths (0.000 ? R ? 0.999). The distribution of possible outcomes of your function are:
1: 167
2: 167
3: 166
4: 167
5: 167
6: 166
So 4 and 6 are always under-represented in the distribution by 1 less than their compatriots. This is true no matter how many decimals you allow, though the distribution gets closer and closer to equal the further towards infinite decimal places you go.
This carries over to all die whose numbers of sides do not factor down to a prime factorization of some exponential values of 2 and 5.
So, then, how can we fix this one, tiny issue in a practical manner that doesn't make our heads hurt or put unnecessary strain on the computer?
Real quick addendum to the above:
Obviously when I put the equation after the example of the 10-sided die, I meant to put a 10*RAND() instead of a 6*RAND(). Oops!
Also, where I have 0.000 ? R ? 0.999, the ?'s are supposed to be less-than-or-equal-to signs but the comments didn't like that. Oh well.
How do you keep adding up the total? I would like to have a cell which keeps adding up the total sum of the two dices, even after a new number is generated in the cells when you refresh or generate new numbers.
So, how do you simulate rolling 12 dice? Do you write int(rand()*6) 12 times?
Is there a simpler way of simulating n dice in Excel?
I've run this code in VBA
Sub generate()
Application.ScreenUpdating = False
Application.Calculation = False
Dim app, i As Long
Set app = Application.WorksheetFunction
For i = 3 To 10002
Cells(i, 3).Value = i - 2
Cells(i, 4).Value = app.RandBetween(2, 12)
Cells(i, 5).Value = app.RandBetween(1, 6) + app.RandBetween(1, 6)
Next
Application.ScreenUpdating = True
Application.Calculation = True
End Sub
But I get the same distribution for both columns 4 and 5
Why ?
@Mohammed
I would expect to get the same distribution as you have effectively used the same function