# Python - Predicting the probability of 6 Heads or Tails in a row from a set sample size

I'm wondering whether there is a way to make this even more efficient or reduce the number of variables.

import random
numberOfStreaks = 0
results = []
tail_streak = ['T'] * 6
sample_size = 1000000
for i, experimentNumber in enumerate(range(sample_size)):
# Code that creates a list of 100 'heads' or 'tails' values.
results.append(random.choice(('H', 'T')))

# Code that checks if there is a streak of 6 heads or tails in a row.
try:
temp = results[i-5:]
if temp == head_streak or temp == tail_streak:
numberOfStreaks += 1
except:
pass

print('Chance of streak: %s%%' % (numberOfStreaks / sample_size))


    # Code that creates a list of 100 'heads' or 'tails' values.
results.append(random.choice(('H', 'T')))


This comment is severely misleading: the code does not create a list of 100 values, it create an infinitely growing list that extends up to sampleSize values by the time the program terminates.

Independently of the misleading comment, this is a bad idea, and can be avoided by limiting the size of the results list in some say (del results[:-6], or results = results[-6:], I'm not sure which is better). This would also obsolete the temp variable, because the results array would no longer contain extra flips.

    try:
temp = results[i-5:]
if temp == head_streak or temp == tail_streak:
numberOfStreaks += 1
except:
pass


Bare except statements are a bad idea. Bare except:pass statements even more so. Among other problems, it means that if you press Ctrl-C while your code is executing that section, the code won't exit.

It's not clear what exception you are trying to catch (results[i-5:] doesn't throw an error if results is less than five items long; it just truncates the list), so I can't suggest a direct replacement, but I would recommend either catching a specific exception, or removing the try-catch entirely.

Python lists natively support negative indexing, so you can simplify results[i-5:] to results[-6:] and remove the i variable entirely. As suggested by the question asker in the comments, this makes the enumerate call unnecessary.

The i variable will then be unused. It's clearer to name variables you don't use as _, so it's easy to tell that they aren't used.

Full code:

import random
numberOfStreaks = 0
results = []
tail_streak = ['T'] * 6
sample_size = 1000000
for _ in range(sample_size):
# Code that generates another 'heads' or 'tails' value
results.append(random.choice(('H', 'T')))

# Code that checks if there is a streak of 5 heads or tails in a row.
results = results[-6:]
if results == head_streak or results == tail_streak:
numberOfStreaks += 1

print('Chance of streak: %s%%' % (numberOfStreaks / sample_size))

• in that case i wouldn't even need enumerate would i, for i in range(samplesize) works – DeltaHaxor Jun 17 at 22:24

# Eliminating useless code

Enumerating a range is really pointless

In [6]: sample_size = 5

In [7]: for i, experimentNumber in enumerate(range(sample_size)):
...:     print(i, experimentNumber)
...:
0 0
1 1
2 2
3 3
4 4


So we can easily replace one by the other. We do not even need to replace as experimentNumber is not used anywhere. Next we notice that i is also used only once where we can replace results[i-5:] by superior construct results[-6:]. We also eliminate the superfluous exception handling. So far this is already covered by @ppperys answer.

# Efficiency

you create a complet list of length sample_size of random values in memory. This is not required and may be a problem on big sample sizes. As you always need the last 6 values only you could go for collections.deque which can maintain a maxlen.

from collections import deque
results = deque(maxlen=6)


For the evaluation made easy we do not use ('H', 'T') but numbers. We do not need to comare with a streak any more but do it arithmetically. Here is the only pitfall - we must check if the queue is filled completely to not accidentally accept a short sequence of zeros.

for _ in range(sample_size):
results.append(random.choice((0, 1)))
if len(results) == 6 and sum(results) in (0, 6):
numberOfStreaks += 1


This not only saves memory but we also get rid of a temporary temp and the predifined head_streak and tail_streak. We notice the magic number 6 appearing multiple times - use a variable. We also make a testable function. We end up with

import random
from collections import deque

def streak_probability(streak_len, sample_size):
results = deque(maxlen=streak_len)
numberOfStreaks = 0
for _ in range(sample_size):
results.append(random.choice((0, 1)))
if len(results) == streak_len and sum(results) in (0, streak_len):
numberOfStreaks += 1
return numberOfStreaks / sample_size

print('Chance of streak: %s%%' % (streak_probability(6, 1000000))


# Algorithm

This simulation will give good results for big numbers of sample_size. However if the sample size was smaller than 6 it will always return 0. As you divide the final streak count by the sample size you indicate, that you would like to get the probability of a streak per "additional" coin toss. So we should fill the queue before starting to count. That way an average of a large number of runs with a small sample size would match a single run of a large sample size. If we prefill we do not have to check the fill state of the queue (yes I filled to the max while one less would be sufficient).

def prefilled_streak_probability(streak_len, sample_size):
results = deque((random.choice((0, 1)) for _ in range(streak_len)), maxlen=streak_len)
numberOfStreaks = 0
for _ in range(sample_size):
results.append(random.choice((0, 1)))
if sum(results) in (0, streak_len):
numberOfStreaks += 1
return numberOfStreaks / sample_size


Now test the difference - we compare the original sample size of 1.000.000 to 100.000 repetitions of sample size 10

s=10
n=100000
print('no prefill')
print('Single big sample - Chance of streak: %s%%' % (streak_probability(6, s*n)))
probs = [streak_probability(6, s) for _ in range(n)]
print('Multiple small samples - Chance of streak: %s%%' % (sum(probs)/len(probs)))

print('with prefill')
print('Single big sample - Chance of streak: %s%%' % (prefilled_streak_probability(6, s*n)))
probs = [prefilled_streak_probability(6, s) for _ in range(n)]
print('Multiple small samples - Chance of streak: %s%%' % (sum(probs)/len(probs)))


we get

no prefill
Single big sample - Chance of streak: 0.031372%
Multiple small samples - Chance of streak: 0.01573599999999932%
with prefill
Single big sample - Chance of streak: 0.031093%
Multiple small samples - Chance of streak: 0.031131999999994574%

• You could use random.choices and drop the loop for shorter and more readable code. – agtoever Jun 19 at 21:53
• @agtoever Right. But I was explicitely going for a smaller memory footprint. If going for speed I would change not only the random function but also the streak counting. – stefan Jun 22 at 7:21