I’m revisiting Python while I’m playing with not-R. It’s been a while.
- the transition from 2.x to 3.x is mostly a settled matter.
- pandas/numpy have matured, taking inspiration from the vectorised operations in R.
- I’ve stopped using
<-assignment in R, in favour of
- pandas has started looking at doing a similar pipe as I’ve seen in JS.
- RStudio have rebranded, partially to court Pythonistas.
- R Markdown support for Python has improved.
I was plugging through Math for Python, where I came across our familiar chaos game. Since last time I’d been thinking about taking both/all paths, rather than randomly selecting a path. In the 3-node case, that means that the number of points triples every iteration, so I’ll start with just one point.
I was also looking at the book by the pandas author, and was happy to see my nice vectorised arrays in numpy :)
Moreso than usual, this code is clunky as hell. I’m feeling around and finding my feet here, so it’s going to be hella ugly.
The following snippet comes from a notebook I was working in VSCode/Jupyter. I’m not getting RStudio to play nicely with Python today, at least not while I’m still in RMarkdown rather than Quatro.1
import matplotlib.pyplot as plt import numpy as np import math def transform_1(x, y): x = 0.5 * x y = 0.5 * y return x,y def transform_2(x, y): x = 0.5*x + 0.5 y = 0.5*y + 0.5 return x,y def transform_3(x,y): x = 0.5*x+1 y=0.5*y return x,y x = np.array() y = np.array() while(len(x) < 1e4): t1x, t1y = transform_1(x,y) t2x, t2y = transform_2(x,y) t3x, t3y = transform_3(x,y) x = np.append(t1x,t2x) x = np.append(x, t3x) y = np.append(t1y, t2y) y = np.append(y, t3y) plt.scatter(x/2,y, s=0.01)
The while loop took forever to run in RStudio, barely any time in Jupyter. Likely R is asking Reticulate to do each iteration of the loop, taking the objects back, and sending them back across? Or something isn’t set up on this machine right yet.↩︎