I have a Iris Dataset & I want to calculate total count of features in my dataset.Which library or function can be used to calculate the result???
Pls help me out.
If what you are asking is the number of columns in the dataset, then this will be a simple way to do so:
len(iris.columns)
Related
I have the data from several Stocks and want to cerate 3D_CNN for trend Prediction(binary classification)
anybody have idea or example for create this model?(the number of layer,polling,con2d,con3d,...)
I want to use keras in python
I have individual values and want to test each values significance against a randomly generated dataset (20 values but could generate more if needed). What is the best statistical test to do this please? Thanks!
I have a population that has an average weight of 5 lbs. I've taken sample of 5879 observations from the population. The sample has a total weight of 410522 lbs. I'm trying to figure out if the sample has a significantly higher average weight than the population. Assuming that the population has a normal distribution. I'm trying to use the proportions_ztest from stats model. I'm not sure if I'm using the counts and nobs variables correctly. Can someone please tell me if I'm using the function correctly, or suggest another function? I'm trying to get the p-value.
code:
import statsmodels.api as sm
cnt=410522
nbs=58759
vL=5
sm.stats.proportions_ztest(cnt,
nbs,
vL,
alternative='larger')[1]
You can use scipy.stats.ttest_1samp(a, popmean) to get t and p_value.
This is a two-sided test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean, popmean.
Read more detail here.
If you want to test if the samples has a significantly higher average weight than the population, you should divide the P-value/2 to get right-tailed P_value.
I've been attempting to program a PowerPivot Workbook that I've been using to calculate a weighted standard deviation.
The problem is that when I use the code:
(the quality metric Q is weighted by the Product Tons for each record to get weighted statistics for variable periods [ie weeks, months, years])
Product Q-St.d:=SQRT((SUMX('Table',((([PRODUCT_Q]-[W_Avg_Q]))^2)*[TOTAL_PRODUCT_TONS]))/(((COUNTX('Table',[Production_Q])-1)*[Product Tons])/COUNTX('Table',[Production_Q])))
It calculates the [W_Avg_Q], which is the weighted average for Q, for each row as it iterates through instead of getting a weighted average for the whole context. I've learned pretty much all my DAX on the job or this site so I'm hoping there's some command to get the weighted average to calculate first. Does anyone know such a command? or another method of getting a weighted standard deviation out of DAX?
I think what you want to do is to declare [W_Avg_Q] a variable and then use it in your formula.
Product Q-St.d :=
VAR WtdAvg = [W_Avg_Q]
RETURN SQRT((SUMX('Table',((([PRODUCT_Q]-WtdAvg))^2)*[TOTAL_PRODUCT_TONS])) /
(((COUNTX('Table',[Production_Q])-1)*[Product Tons])/COUNTX('Table',[Production_Q])))
This way it gets calculated once in the proper context and then stored and reused within the formula.
I would like to use the Student T cumulative distribution function & its inverse function in a measure related to a data table in PivotTable Fields.
Excel has a T CDF with T.DIST and its inverse with T.INV. However is it a more or less straightforward to use an equivalent in DAX?
I cannot currently find any answer to my question.
Thanks in advance for your insights!