Explore the normal distribution: a histogram built from samples and the PDF (probability density function). Import numpy as np # Sample from a normal distribution using numpy's random number generator. The frozen form creates an object with the distribution parameters set. For example, you could evaluate the PDF of a normal(3, 4) distribution at the value 5. Stats.norm.pdf(5, 3, 4). Mydist = stats.norm(3, 4) mydist.pdf(5) Note that the argument of the PDF, in this example 5, comes before the distribution parameters.
Active7 months ago
I am looking for a function in Numpy or Scipy (or any rigorous Python library) that will give me the cumulative normal distribution function in Python.
Salvador Dali
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toma
8 Answers
Here's an example:
In other words, approximately 95% of the standard normal interval lies within two standard deviations, centered on a standard mean of zero.
If you need the inverse CDF:
Alex ReynoldsAlex Reynolds
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It may be too late to answer the question but since Google still leads people here, I decide to write my solution here.
That is, since Python 2.7, the
math library has integrated the error function math.erf(x)
The
erf() function can be used to compute traditional statistical functions such as the cumulative standard normal distribution:
Ref:
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Adapted from here http://mail.python.org/pipermail/python-list/2000-June/039873.html
UnknownUnknown
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To build upon Unknown's example, the Python equivalent of the function normdist() implemented in a lot of libraries would be:
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CerinCerin
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Alex's answer shows you a solution for standard normal distribution (mean = 0, standard deviation = 1). If you have normal distribution with
mean and std (which is sqr(var) ) and you want to calculate:
Read more about cdf here and scipy implementation of normal distribution with many formulas here.
Salvador DaliSalvador Dali
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Starting
Python 3.8 , the standard library provides the NormalDist object as part of the statistics module.
It can be used to get the cumulative distribution function (
cdf - probability that a random sample X will be less than or equal to x) for a given mean (mu ) and standard deviation (sigma ):
Which can be simplified for the standard normal distribution ( Xavier GuihotXavier Guihot
mu = 0 and sigma = 1 ):
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David MillerDavid Miller
As Google gives this answer for the search netlogo pdf, here's the netlogo version of the above python code
Andro Selva
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platipodiumplatipodium
Active7 months ago
How to generate a random integer as with
np.random.randint() , but with a normal distribution around 0.
np.random.randint(-10, 10) returns integers with a discrete uniform distributionnp.random.normal(0, 0.1, 1) returns floats with a normal distribution
What I want is a kind of combination between the two functions.
bakkal
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Ghilas BELHADJGhilas BELHADJ
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4 Answers
One other possible way to get a discrete distribution that looks like the normal distribution is to draw from a multinomial distribution where the probabilities are calculated from a normal distribution.
Here,
np.random.choice picks an integer from [-10, 10]. The probability for selecting an element, say 0, is calculated by p(-0.5 < x < 0.5) where x is a normal random variable with mean zero and standard deviation 3. I chooce std. dev. as 3 because this way p(-10 < x < 10) is almost 1.
The result looks like this:
ayhanayhan
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Python Normal Distribution Density Function
It may be possible to generate a similar distribution from a Truncated Normal Distribution that is rounded up to integers. Here's an example with scipy's truncnorm().
Let's see what it looks like
Community♦
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The accepted answer here works, but I tried Will Vousden's solution and it works well too:
vs97
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stephanstephan
Here we start by getting values from the bell curve.
CODE:
OUTPUT:
CopyPasteItCopyPasteIt
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