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Questions - standard-deviation

Convert Data Frames into One Column Vector

I have an 11 x 8 data frame of numeric values in R that I want to find the standard deviation of. However, I cannot take the standard deviation of a matrix (use the sd() function), only the columns. B...
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numericalorange

r

dataframe

standard-deviation

Votes: 0

Answers: 1

Latest Answer

#generate data df <- data.frame(matrix(rbinom(8*11, 1, .5), ncol=8)) #get sd sd(unlist(df)) edit: just saw the comment where user fra got there first
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frustrated_bioinformatician

Is there a way to find a deviation between a1=[] and a2=[] in Python?

We have these arrays in python: a1 = [0, 1, 2, 3] a2 = [0, 1.1, 2.2, 3.1] and want to find the deviation between a1 and a2. And sorry, we do not know what's such a type of deviation is named, but pro...
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akakakakakkaa

python

arrays

statistics

standard-deviation

deviation

Votes: 0

Answers: 1

Latest Answer

You can find the sum of squared distances (as clarified in the comments) by using sum() and zip(): distance = sum([(elem1 - elem2)**2 for elem1, elem2 in zip(a1, a2)])
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BrokenBenchmark

Inconsistent results from pandas.rolling.std()

As you can see, the code is from the PANDAS official example, the STD of the last 3 numbers(5,5,5) should be 0, but it's not in the example. In [1]: s = pd.Series([5,5,6,7,5,5,5]) In [2]: s.rolling(3...
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Jake

python

pandas

standard-deviation

Votes: 0

Answers: 1

Latest Answer

What you see is the result of small rounding errors in the floating point calculations done when calculating the standard deviation with a rolling window. In earlier versions of pandas, the code to ca...
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Craig

Calculate standard deviation for groups of values using Python

My data looks similar to this: index name number difference 0 AAA 10 0 1 AAA 20 10 2 BBB 1 0 3 BBB 2 1 4 CCC 5 0 5 CCC 10 5 6 CCC 10.5 0...
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vbadvd

python

pandas-groupby

aggregate

standard-deviation

Votes: 0

Answers: 1

Latest Answer

You can use groupby(['name']) on the full data frame first, and only apply the agg on the columns of interest: data = pd.DataFrame({'name':['AAA','AAA','BBB','BBB','CCC','CCC','CCC'], ...
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StupidWolf

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