Comparing R and Python sequences

This is a post about elementary sequence operations in R and Python. It's as much for me as for you.

The most obvious difference between sequences in R and Python is Python's use of 0-based indexing:

R:

> 1:5 [1] 1 2 3 4 5 > A = seq(1,10,by=2) > A [1] 1 3 5 7 9 > A[2] [1] 3

Python:

>>> range(1,6) [1, 2, 3, 4, 5] >>> A = range(1,11,2) >>> A [1, 3, 5, 7, 9] >>> A[1] 3

Another difference is that in R, but not in Python, one can assign to an index outside the initial range:

> m = 1:2 > m[6] = 35 > m [1] 1 2 NA NA NA 35

>>> m = range(1,3) >>> m[6] = 35 Traceback (most recent call last): File "", line 1, in IndexError: list assignment index out of range

In R, but not in regular Python, we can make the increments non-integral values:

> A = seq(0,20,by=0.1) > A[1] [1] 0 > length(A) [1] 201 > A[length(A)] [1] 20

We can use numpy to get around this restriction:

>>> import numpy as np >>> A = np.arange(0,20.1,0.1) >>> A[0] 0.0 >>> len(A) 201 >>> A[-1] 20.0

It's sometimes more convenient to specify how many numbers we want to obtain (evenly spaced in some interval):

> A = seq(0,2,length=6) > A [1] 0.0 0.4 0.8 1.2 1.6 2.0

>>> A = np.linspace(0,2,6) >>> A array([ 0. , 0.4, 0.8, 1.2, 1.6, 2. ])

Vectorized operations:

> m = 1:9 > dim(m) = c(3,3) > m [,1] [,2] [,3] [1,] 1 4 7 [2,] 2 5 8 [3,] 3 6 9 > m = t(m) > m [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 [3,] 7 8 9 > apply(m,1,mean) [1] 2 5 8 > apply(m,2,mean) [1] 4 5 6 > mean(m) [1] 5

>>> m = np.arange(1,10) >>> m.shape = (3,3) >>> m array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> np.mean(m, axis=0) array([ 4., 5., 6.]) >>> np.mean(m, axis=1) array([ 2., 5., 8.]) >>> np.mean(m) 5.0

Here are some examples of fancy indexing where we rearrange rows and columns both at the same time:

> m [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 [3,] 7 8 9 > m[c(2,3,1),c(3,2,1)] [,1] [,2] [,3] [1,] 6 5 4 [2,] 9 8 7 [3,] 3 2 1

The naive implementation in Python gives something different than in R (though useful), but the sequential approach works:

>>> m array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> m[[1,2,0],[2,1,0]] array([6, 8, 1]) >>> m[[1,2,0],:][:,[2,1,0]] array([[6, 5, 4], [9, 8, 7], [3, 2, 1]])

R has a few more indexing tricks for which I don't know if there is a Python equivalent:

> m = 1:9 > dim(m) = c(3,3) > m = t(m) > m[-1,] [,1] [,2] [,3] [1,] 4 5 6 [2,] 7 8 9 > sel = m[1,] > 2 > sel [1] FALSE FALSE TRUE > m[sel] [1] 7 8 9 > y = -5:5 > y [1] -5 -4 -3 -2 -1 0 1 2 3 4 5 > y[y < 0] <- -y[y < 0] > y [1] 5 4 3 2 1 0 1 2 3 4 5 > y <- abs(y) > y [1] 5 4 3 2 1 0 1 2 3 4 5

But here, finally is an example we can do in both:

> m [,1] [,2] [,3] [1,] 1 2 3 [2,] 4 5 6 [3,] 7 8 9 > sel = array(c(1:3,3:1), dim=c(3,2)) > sel [,1] [,2] [1,] 1 3 [2,] 2 2 [3,] 3 1 > m[sel] = 0 > m [,1] [,2] [,3] [1,] 1 2 0 [2,] 4 0 6 [3,] 0 8 9

>>> m array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> m[[0,1,2],[2,1,0]] = 0 >>> m array([[1, 2, 0], [4, 0, 6], [0, 8, 9]])