UPDATE: While I’m already half way through the much recommended book by Zed A. Shaw – Learn Python The Hard Way, I’m still doing my research on other great resources to help me get started with Python. This page listing 10 Python blogs worth following, in particular emphasizes Mouse vs python to be the most useful. Starting the 10th of June, I’ll be engaged on a 9-week-long MOOC on Computer Science using Python, offered by MIT.
It’s been 2 months since I got started with R, and although my progress seems fast to me, it appears so mainly because R comes with insanely helpful packages that reduce large chunks of code into simple functions. Not only that, data visualization and graphics generated in R are beautiful and elegant. For example, the following code generates a Mandelbrot set created through the first 50 iterations of equation z = z2 + c plotted for different complex constants c
library(caTools) # external package providing write.gif function jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")) m <- 1000 # define size C <- complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ), imag=rep(seq(-1.2,1.2, length.out=m), m ) ) C <- matrix(C,m,m) # reshape as square matrix of complex numbers Z <- 0 # initialize Z to zero X <- array(0, c(m,m,50)) # initialize output 3D array for (k in 1:50) { # loop with 50 iterations Z <- Z^2+C # the central difference equation X[,,k] <- exp(-abs(Z)) # capture results } write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=800)
This is just an illustration of the power of a dozen or so lines of R code. Just as there are a ridiculous many packages in R, there are countless modules packed into many thousands of packages in Python to make life simpler, so I wasn’t surprised to find a module called antigravity, that can be imported in Python like this:
import antigravity
and voila, you are redirected to this telling XKCD tale of Cueball performing gravity-defying stunts with Python.
Discovering Python & R 