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SCIPY BINOMIAL CODE

In probability and statistics, a realization, or observed value, of a When you use np.random.binomial(n, p, 1), it is just a realization of the random variable ( binom(n, p)): Scipy generates a random variable while numpy generates random numbers. I found reference to some basic difference between the 2 modules: Difference between random draws from and numpy.random What additional functionality is provided by scipy library that is not there in numpy?Ĭomplete list of methods in each module is here: So, my question is what is the primary motive to have 2 copies of the same distribution functions?

SCIPY BINOMIAL CODE

Going over the code I found scipy uses numpy internally : I found that pretty much the same functions are present in both scipy and numpy. Uniform, Binomial, Bernoulli, normal distributions Since this only happens one our of four times, we expect the result to be 25%.I was going over some distribution functions at python: While it's a bit strange to ask "what percentage of results have 0.2 or fewer heads?" since we cannot get a partial number of heads, but it's easy to calculate that the only number of heads that is equal to or less than 0.2 is getting zero heads.

COIN.cdf(2) asks "what percentage of results have 2 or fewer heads?".

COIN.cdf(1) asks "what percentage of results have 1 or fewer heads?".

COIN.cdf(0.2) asks "what percentage of results have 0.2 or fewer heads?".

Using our two-coin flip example where COIN = binom(n=2, p=0.5), the CDF functions are asking the following:

Graphically, this is the the total area of everything less than or equal to x (**the total area of the left of x*).

The probability of all outcomes less than or equal to a given value x,.

The Cumulative Distribution Function or CDF is: We can represent this distribution as a table and a graph: This is represented when COIN returns the value 2 ( exactly two heads).

There is a 25% probability of the outcome having two heads ( HH).

This is represented when COIN returns the value 1 ( exactly one head).

There is a 50% probability of the outcome having exactly one head ( TH or HT).

This is represented when COIN returns the value 0 ( zero heads).

There is a 25% probability of the outcome having zero heads ( TT).

The binomial distribution models these outcomes:

There are four possible outcomes - HH, HT, TH, and TT. To create this distribution in Python: from scipy. The functions are the same no matter what distribution you have - so let's discovery them via examples! Example Binomial DistributionĪ simple binomial distribution that is easy to understand is a binomial distribution with n=2 and p=0.5 (two events, each with a 50% chance of success, like flipping a coin two times and finding out how many times we get heads). Once you have a variable with a distribution, there are many Python functions we can use to preform calculations with the distribution. Three extremely common distributions are normal, bernoulli and binomial distributions: Distributionī = bernoulli (p = 0.2 ) # Bernoulli Distribution with p=0.2ĭ = binom (p = 0.5, n = 2 ) # Binomial Distribution with p=0.1, n=50 The scipy.stats library in Python provides us the ability to represent random distributions using Python! The library has dozens of distributions, including all commonly used distributions.