A short, fun one for this week:. Two players are playing a game where they flip a not necessarily fair coin, starting with Player 1. The first person to flip heads wins. The probability that a coin flipped lands on heads is p. What is the probability that Player 1 will win the game?

We can explore this problem with a simple function in python. We can simulate this game with the following code:. We begin by importing numpy, as we can utilize its random choice functionality to simulate the coin-flipping mechanism for this game. We define the name of our function, and specify our two arguments. Obviously, we can enter custom probabilities here as we like. We also set a global counter for the number of games won by Player 1, to begin at 0 for each round of simulations.

Now, for every game in the specified number of simulations e. This both keeps track of how the long the game has gone on, and will help us to determine who the winner is once a heads has been flipped.

While win remains equal to zero, the players continue to flip the coin. We can understand this in the following way: if the probability of flipping a heads is 0. Once the winning condition is met, we check how many times the coin has been flipped.

Because player 1 always has the first flip, we know that an odd number of flips means that player 1 was the last player to go, and would therefore be the winner. Once all simulations have been run, the function returns the number of games won by player 1 divided by the total number of games played.

This will be expressed as a decimal win percentage. Over 50, games, we see that player 1 has a distinct advantage by going first. What if we adjust the probability of the coin turning up heads? What if we really scale back the likelihood of a head appearing? Fun stuff! Play around with the function as you like to see how different numbers of simulations and probabilities for heads effect the likelihood of player 1 winning.

Sign in. Michael Salmon Follow. Towards Data Science A Medium publication sharing concepts, ideas, and codes. Python Numpy Probability. Aspiring data scientist. Still figuring my life out. Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes. Write the first response.Hi everyone.

I am just learning Python on class so I am really at the basic. I need to write a python program that will flip a coin times and then tell how many times tails and heads were flipped. This is what I have so far but I keep getting errors. Problem is it returns this to me: Out of flips 0 were heads and were tails. Also everything is indented I just couldn't get it to show up in the post.

### How To Code A Fair Coin Flip In Python — Regina Of Tech

This script should just return a NameError and not run. Here's a fixed version:. Hope this helped! Yes, Arrorn, but she said she was at a basic level, and it sounds like this program isn't really going to need much future expansion. It's a simple probability program consisting of very few lines. And even if it did, rewriting it requires pretty much no time to do. Plus, I know your code isn't very advanced either, but at a basic level, most people have trouble handling such large blocks of code together.

I hope the new program works for you Stephanie! You should try to work your way through Arrorn's code though, it's a good resource too, and a great example of another step up. I would avoid using the Python shell to write multiline programs. This would have given you an error message like: NameError: name 'timesflipped' is not defined. What kind of a grade did you get. Hopefully your teacher did not give a passing one. Run this modified version and see if you can tell why you will always get approximately twice as many tails as heads.

I did get a passing mark and that wasnt a bad code : but maybe you were right idko and its not a he.Tossing a one or more coins is a great way to understand the basics of probability and how to use principles of probability to make inference from data.

Let us simulate coin toss experiment with Python. Let us say we have a fair coin and toss the coin just once. We will observe either a head or a tail. We can code 1 for head and 0 for tail. Let us simulate a single fair coin toss experiment with the binomial distribution function in Python. We can easily repeat the coin toss experiment multiple times by changing n.

In the above experiment, we used a fair coin. We can easily simulate an unfair coin by changing the probability p. That is our single experiment.

Often to see how reliable our coin toss experiment is we might want to repeat the experiment multiple times or think of throwing multiple coins. Let us repeat our coin toss experiment times, where in each experiment we toss a fair coin 10 times. Let us ask how many heads we see in each of the experiments.

We started with a simple experiment, tossing a far coin 10 times. We can use the number of successes heads observed in many ways to understand the basics of probability.

For example, we can simply count how many times we see 0 heads, 1 head, 2 heads with our fair coin toss, and so on. We can see that, in our experiments we never saw all heads and all tails with our fair coin as the first and last element are zero.

We can also see that we observe more times 4, or 5, or 6 heads. The above success counts sums toour total number of experiments. We can see from the above plot that probability of seeing 4 heads is the highest, closely followed by probability of seeing 5 heads.

Note that this observation might vary depending on the realization of our random simulation. We know that it is a fair, so we would expect that if we repeat the experiment many more times we should see that probability of seeing 5 heads should be the highest.

So, let us repeat our coin toss experimenttimes and compute the probability of seeing n heads like above. Now we can see that probability of seeing 5 heads is the highest as we expected. Let us do our experiment with biased coins.

**Coin flip game Python**

Let us say we have a coin and we suspect it is a biased coin. Let us what we can infer about how unfair the coin is by repeated experiments like before. Just as described before, let us toss the unfair coin 10 times, repeat it fortimes, and count the number of successes. Let us use the number of successes to get the probability of x successes and plot it.

Email Address. Share this: Twitter Facebook. Return to top of page.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Code Review Stack Exchange is a question and answer site for peer programmer code reviews. It only takes a minute to sign up. I think I finally finished a small coin flip project I found online. However, I'm pretty confident this is far from the optimal solution and would really appreciate any feedback on how this could be improved.

Since I'm studying by myself using Head First and Code Academy any feedback would be very very helpful! This is my first time really using GitHub, but I included the. This is pretty exact text to expect from the user. I'd at least upper-case their input, and unless you really needed them to be specific, only keep the first letter:. Regardless of if they were right or wrong though, they still made a guess.

But then you're also only adding to guesses on wrong answers too. If you change it to append to guesses on both right and wrong answers, you'll have this:.

If you need to find out how many guesses were made, you can just do len guesses. To check if it was their first guess or not, you can do:. I think that whole game loop part should be taken out into it's own function though; beyond what you've already done.

I'd have a function that returns a list of guesses made until a correct guess was made. With how you have it set up now, I'm going to disregard cancelling since that will require some broader changes to accommodate, and it's highly unlikely that the user will have to guess more than a few times. The bulky bit was taken out so it can be dealt with as an isolated bit of code.

To know the outcome of the game, all I care about besides cancelling, which could be handled by returning None is the list of guesses they made until they won. I can get all the information I need from just that. Needing to mix in winnerand cancel flags can suggest that that code should be moved out. By the time you're managing two or three flags to control execution in one block of code, it can become hard to follow.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am VERY new to Python and I have to create a game that simulates flipping a coin and ask the user to enter the number of times that a coin should be tossed. I suggest you read this on for loops. Also, you could pass number as a parameter to the function :. You could try this, i have it so it asks you if you want to flip the coin then when you say no or n it tells you how many times you flipped the coin.

Create a list with two elements head and tail, and use choice from random to get the coin flip result. Use uin to call. Learn more. Python Coin Toss Ask Question. Asked 7 years, 1 month ago. Active 2 months ago. Viewed 52k times.

## Simulate coin flipping in python

YeahScience YeahScience 1 1 gold badge 1 1 silver badge 3 3 bronze badges. There are better options in random but I presume you have to solve the problem long hand so to speak. Can you give some more information? What specifically is the problem?

Active Oldest Votes. You need a loop to do this. Rushy Panchal Rushy Panchal 13k 11 11 gold badges 47 47 silver badges 83 83 bronze badges. You are nearly there: 1 You need to call the function: coinToss 2 You need to set up a loop to call random.

Donato Szilagyi 3, 3 3 gold badges 31 31 silver badges 48 48 bronze badges. Aitch Aitch 31 1 1 bronze badge. This is possibly more pythonic, although not everyone likes list comprehensions. John Powell John Powell BallisticZer0 BallisticZer0 49 2 2 bronze badges. Counter y. Abhishek Bahukhandi Abhishek Bahukhandi 11 2 2 bronze badges. Go 1, 4 4 gold badges 11 11 silver badges 20 20 bronze badges. Panagiotis Ntinis Panagiotis Ntinis 16 3 3 bronze badges.To master anything, you need to start at the beginning, understanding the basics.

Coding a fair coin flip is kind of a right of passage when it comes to python. It seems like a simple project, but it can be done in many different ways. You may even get an insight into your coding habits. Being able to critique your code is a critical programmer skill, but that is another post. Yes, even with a simple project like this, habits are made in and out of the public eye. Your standard is only as high as when no one is looking.

We know that we will be doing a fair coin flip. A coin is made up of two halves, head and tails. If the description mentioned biased or weighted coin then the probability would be adjusted. How are we going to simulate a coin flip? Since we are using python, a mathematically focused language, we will use probability. This is a method in the random class and it takes in the number of trials n and the probability of the event occurring p.

## Probability in a Weighted Coin-flip Game using Python and Numpy

The call returns a 0 or 1 to represent one of the two events. Here is the equation that it uses:. We know now that for the binomial distribution to work we need two variables, n and p.

This opens the question, do we want to see each flip and track it or are we satisfied with just the number of times a positive 1 event occurred? So we will need an array to store the results in. Thank goodness numpy can do all of that! First things first, we need to import numpy. We know that we will need those since we will be using np. In my code, I like to mark where the main area is for easy searching just in case there are several methods created in that program.

Marking any variables that can be safely changed without any negative effects is a great practice to get into as well.

We also know that we will need to initiate an array to store the flips. Using np. This is the point where we can go down two paths. Since we are about code re-usability we will create a function that houses the binomial distribution.

This will allow for easy access if we decide to build upon the program in the future. We will call it coinFlip and it will need to take in the probability for the events.

It will return the result from the binomial.A short, fun one for this week:. Two players are playing a game where they flip a not necessarily fair coin, starting with Player 1. The first person to flip heads wins. The probability that a coin flipped lands on heads is p. What is the probability that Player 1 will win the game? We can explore this problem with a simple function in python.

We can simulate this game with the following code:. We begin by importing numpy, as we can utilize its random choice functionality to simulate the coin-flipping mechanism for this game. We define the name of our function, and specify our two arguments. Obviously, we can enter custom probabilities here as we like. We also set a global counter for the number of games won by Player 1, to begin at 0 for each round of simulations.

Now, for every game in the specified number of simulations e.

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This both keeps track of how the long the game has gone on, and will help us to determine who the winner is once a heads has been flipped. While win remains equal to zero, the players continue to flip the coin. We can understand this in the following way: if the probability of flipping a heads is 0. Once the winning condition is met, we check how many times the coin has been flipped. Because player 1 always has the first flip, we know that an odd number of flips means that player 1 was the last player to go, and would therefore be the winner.

Once all simulations have been run, the function returns the number of games won by player 1 divided by the total number of games played. This will be expressed as a decimal win percentage. Over 50, games, we see that player 1 has a distinct advantage by going first. What if we adjust the probability of the coin turning up heads?

What if we really scale back the likelihood of a head appearing? Fun stuff! Play around with the function as you like to see how different numbers of simulations and probabilities for heads effect the likelihood of player 1 winning.

Sign in. Michael Salmon Follow. Towards Data Science A Medium publication sharing concepts, ideas, and codes. Python Numpy Probability.

Aspiring data scientist. Still figuring my life out. Towards Data Science Follow. A Medium publication sharing concepts, ideas, and codes.

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Coin flip python