![]() Let’s learn a little more about these parameters: While the function only has three parameters, it provides significant opportunity to customize the returned array. Size=None # The size or shape of your array Let’s take a look at how the function works: # Understanding the syntax of random.normal() Under the hood, Numpy ensures the resulting data are normally distributed. The function is incredible versatile, in that is allows you to define various parameters to influence the array. The numpy random.normal function can be used to prepare arrays that fall into a normal, or Gaussian, distribution. How to Use Numpy to Create a Normal Distribution According to a Gaussian distribution, ~68.2% of values will fall within one standard deviation. In the image above, the dark blue lines represent 1 standard deviation from the mean in both directions. They follow conventions around standard deviations.When we say that data are distributed normally, we mean: Similarly, blood pressure, marks on a test, and items produced by machinery. For example, heights and weights of people are generally normally-distributed. This content is taken from DataCamp’s Statistical Thinking in Python (Part 1) course by Justin Bois.You might be thinking to yourself, “how often can this actually happen?” It has a lot, however. To learn more about random number generators and hacker statistics, please see this video from our course Statistical Thinking in Python (Part 1). When we run the above code, it produces the following result: # Generate random numbers by looping over range(100000) ![]() ![]() # Initialize random numbers: random_numbers It is not necessary to label the axes in this case because we are just checking the random number generator. ![]()
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