In my previous post I noted that I would provide python code for the chart that is in the post. The chart was created using R statistical software, and the code for the python version can be found at the end of this post.

I find python a bit less intuitive than R but that’s most likely because I’ve been using R for a very long time and python for less long. There are reasons, I suppose, to favour one over the other, but for statistical analysis and data analysis I don’t necessarily see an advantage of one over the other. That being said, it is my sense that R does a better job of standardizing across various operating systems, which can be very helpful when you are a Linux user, as am I.

```
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from matplotlib.animation import FuncAnimation
plt.style.use('ggplot') # this is to make the plot look like an R ggplot
# a roulette array
roulette = np.append(np.array([0, 0]),np.arange(1, 37))
spins1000 = np.array(np.random.choice(roulette, size=(1000)))
# Define a cumulative mean function
def cum_mean(arr):
cum_sum = np.cumsum(arr)
return cum_sum / (np.arange(1, cum_sum.shape[0] + 1)) # as far as I can tell, matplotlib doesn't have a cumulative mean function; so I created one.
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 2)
ax2 = fig.add_subplot(2, 1, 1)
fig.tight_layout(pad=3.0)
fig.suptitle('Short-term Randomness versus Long-term Predictability', fontsize=14)
ax2.set_xlabel('$n^th$ spin of roulette wheel')
ax2.set_ylabel('Value of $n^{th}$ spin')
ax2.set_xlim(0, 1000)
ax2.set_ylim(0, 37)
ax1.set_xlabel('$n^{th}$ spin of the roulette wheel')
ax1.set_ylabel('Cumulative mean of n spins')
ax1.set_xlim(0, 1000)
ax1.set_ylim(0, 37)
line, = ax1.plot([], [], lw=1.5)
scat, = ax2.plot([], [], 'o', markersize=2)
def init():
line.set_data([], [])
scat.set_data([], [])
return line, scat,
def animate(i):
x = np.linspace(0, 250, 250)
y1 = cum_mean(spins1000)
y2 = spins1000
line.set_data(x[:i], y1[:i])
scat.set_data(x[:i], y2[:i])
return line, scat,
anim = FuncAnimation(fig, animate, init_func=init, frames=1000, interval=10, blit=True, save_count=1000)
plt.show()
anim.save('roullete_python.mp4') # saving as .mp4 because python creates massive gif files.
```