I want to know how obtain with python 2.7 the middle number in a range like but with a predefined grid per example of 40 per 40 (multiple of 40):
0, 600 the number will be 320 and not 300 because 300 is not a multiple of 40...
0, 300 the number will be 160 and not 150 because 150 is not a multiple of 40...
Any help will be appreciated...
EDIT
i want a function or something like that not myself calculating...
something like this?
def mean_mod(a, b, md=40):
return md * ( ((a+b)//2) // md )
print(mean_mod(0, 600))
print(mean_mod(0, 300))
output:
280
120
(rounds down...)
Related
I am trying to create a list of the 99th and 1st percentiles. Rather than a single percentile for today. I wanted percentiles for 500 days each using the prior 500 days. The functions I was using for this are the following
swin:{[f;w;s] f each { 1_x,y }\[w#0;s]}
percentile:{[x;y] y (100 xrank y:asc y) bin x}
swin[percentile[99;];500;List].
The issue I come across is that the 99th percentile calculates perfectly, but the 1st percentile makes the entire list = 0. a bit lost as to why it would do that. suggestions appreciated!
What's causing the zeros is two-fold:
What behaviour do you want for the earliest 500 days when there isn't 500 days of history to work with? On day 1 there's only 1 datapoint, on day 2 only 2 etc. Only on the 500th day is there 500 days of actual data to work with. By default that swin function fills the gaps with some seed value
You're using zero as that seed value, aka w#0
For example a 5 day lookback on each date looks something like:
q)swin[::;5;1 2 3 4 5]
0 0 0 0 1
0 0 0 1 2
0 0 1 2 3
0 1 2 3 4
1 2 3 4 5
You have zeros until you have data, so naturally the 1st percentile will pick up the zeros for the first roughly 500 dates.
So then you can decide to seed with a different value, or else possibly exclude zeros from your percentile function:
q)List:1000?1000
q)percentile:{[x;y] y (100 xrank y:asc y except 0) bin x}
q)swin[percentile[1;];500;List]
908 360 360 257 257 257 90 90 90 90 90 90 90 90...
If zeros are a legitimate value in your list and can't be excluded then maybe seed the swin with some other value that you know won't be in the list (negatives? infinity? null?) and then exclude that seed from the percentile function.
EDIT: A final alternative is to use a different sliding window function which doesn't fill gaps with a seed value, e.g.
q)swin2:{[f;w;s] f each(),/:{neg[x]sublist y,z}[w]\[s]}
q)swin2[::;5;1 2 3 4 5]
,1
1 2
1 2 3
1 2 3 4
1 2 3 4 5
q)percentile:{[x;y] y (100 xrank y:asc y) bin x}
q)swin2[percentile[99;];500;List]
908 908 908 908 908 908 908 908 908 908 908 959 959..
q)swin2[percentile[1;];500;List]
908 360 360 257 257 257 90 90 90 90 90 90 90 90 90..
I have a star schema data model. DimDate, DimBranchName, BranchActual, BranchBudget.
I have measures to calculate the YTD variance to Budget by Branch called QVar. Qvar takes the counts from BranchActual and compares it BranchBudget between two dates. The visual is controlled by DimBranchName and DimDate.
Current Result:
BranchName YTDActual YTDBudget QVar
A 100 150 (33%)
B 200 200 0.0%
C 25 15 66%
I want a measure to be able to rank DimBranchName[BranchName] by QVar that will interact with the filters I have in place.
Desired result:
BranchName YTDActual YTDBudget QVar Rank
A 100 150 (33%) 3
B 200 200 0.0% 2
C 25 15 66% 1
What I've tried so far is
R Rank of Actual v Goal =
var V = [QVar]
RETURN
RANKX(ALLSELECTED('BranchActual'),CALCULATE(V),,ASC,Dense)
What I get is all 1's
BranchName YTDActual YTDBudget QVar Rank
A 100 150 (33%) 1
B 200 200 0.0% 1
C 25 15 66% 1
Thanks!
When you declare a variable it is computed once and treated as a constant through the rest of your DAX code, so CALCULATE(V) is simply whatever V was when you declared the variable.
This might be closer to what you want:
R Rank of Actual v Goal =
RANKX ( ALLSELECTED ( DimBranchName[BranchName] ), [QVar],, ASC, DENSE )
This way [QVar] is called within the filter context of the BranchName instead of being a constant. (Note that referencing a measure within another measure implicitly wraps it in CALCULATE so you don't need that again.)
I am currently trying to create a report that shows how customers behave over time, but instead of doing this by date, I am doing it by customer age (number of months since they first became a customer). So using a date field isn't really an option, considering one customer may have started in Dec 2016 and another starts in Jun 2017.
What I'm trying to find is the month-over-month change in units purchased. If I was using a date field, I know that I could use
[Previous Month Total] = CALCULATE(SUM([Total Units]), PREVIOUSMONTH([FiscalDate]))
I also thought about using EARLIER() to find out but I don't think it would work in this case, as it requires row context that I'm not sure I could create. Below is a simplified version of the table that I'll be using.
ID Date Age Units
219 6/1/2017 0 10
219 7/1/2017 1 5
219 8/1/2017 2 4
219 9/1/2017 3 12
342 12/1/2016 0 500
342 1/1/2017 1 280
342 2/1/2017 2 325
342 3/1/2017 3 200
342 4/1/2017 4 250
342 5/1/2017 5 255
How about something like this?
PrevTotal =
VAR CurrAge = SELECTEDVALUE(Table3[Age])
RETURN CALCULATE(SUM(Table3[Units]), ALL(Table3[Date]), Table3[Age] = CurrAge - 1)
The CurrAge variable gives the Age evaluated in the current filter context. You then plug that into a filter in the CALCULATE line.
Here is the problem I am working on:
You are to develop a menu-driven program that will allow the analyses of data from the file Patient_Data.txt using the following equations:
Half-Life Equations
Ct = C0e^-kt
t½ = ln(2)/k
where:
Ct is the concentration in ug/L at time t
C0 is the initial concentration in ug/L
t is the time in hrs
k is the time constant (1/hrs)
t½ is the half-life in hrs
The user of the program must be able to obtain the average half-life (to 2 decimal places) along with the number of measurements used to calculate the average for any of the 5 patients for which data has been collected.
The program must also be able to display the 2 patient numbers and averages of the patients that have the highest half-life average values.
A menu must be used to select the different options with an additional option for Exit. The program must run until exit is selected by the user.
The program must be designed using functions.
A function called analyzeData must take as input the patient number and must return both the average half-life and the number of measurements in the average for the input patient number.
A separate function called halfLife is to be used for calculating the t½ (half-life) based on C0 (initial concentration), Ct (concentration at time t) and t (time) that are in the data file.
A third function called highest2halfLifes must also be used to determine the two patients with the longest average half-life from the five different patients. All four values (patient1, halfLife1, patient2, halfLife2) must be returned to the main function.
The following data file Patient_Data.txt lists values for C0, Ct, and t, respectively (Patient Data)
1 325 160 2.0
1 600 100 6.2
2 325 220 1.0
3 600 200 4.4
4 325 100 3.0
4 325 88 3.2
2 600 200 3.3
2 325 100 3.3
4 600 210 3.4
5 325 105 3.5
1 600 110 6.0
3 325 100 3.1
2 600 120 5.5
2 600 125 5.5
5 120 60 2.2
2 325 100 3.4
I have a data frame that looks like this:
id age sallary
1 16 500
2 21 1000
3 25 3000
4 30 6000
5 40 25000
and a list of ids that I would like to ignore [1,3,5]
how can I get a data frame that will contain all the remaining rows: 2,4.
Big thanks for every one.
Call isin and negate the result using ~:
In [42]:
ignore_ids=[1,3,5]
df[~df.id.isin(ignore_ids)]
Out[42]:
id age sallary
1 2 21 1000
3 4 30 6000