HTML tutorial
CSS3 tutorial
Bootstrap tutorial
JavaScript tutorial
JQuery tutorial
AngularJS tutorial
React tutorial
NodeJS tutorial
PHP tutorial
Python tutorial
Python3 tutorial
Django tutorial
Linux tutorial
Docker tutorial
Ruby tutorial
Java tutorial
C tutorial
C ++ tutorial
Perl tutorial
JSP tutorial
Lua tutorial
Scala tutorial
Go tutorial
ASP.NET tutorial
C # tutorial
In the previous chapter we learned how to create a completely random array, of a given size, and between two given values
In the previous chapter we learned how to create a completely random array, of a given size, and between two given values.
In this chapter we will learn how to create an array where the values are concentrated around a given value.
In probability theory this kind of data distribution is known as the normal data distribution, or the Gaussian data distribution, after the mathematician Carl Friedrich Gauss who came up with the formula of this data distribution.
A typical normal data distribution:
import numpy
import matplotlib.pyplot as plt
x =
numpy.random.normal(5.0, 1.0, 100000)
plt.hist(x, 100)
plt.show()
Note: A normal distribution graph is also known as the bell curve because of it's characteristic shape of a bell.
Histogram Explained
We use the array from the numpy.random.normal()
method, with 100000 values, to draw a histogram with 100 bars.
We specify that the mean value is 5.0, and the standard deviation is 1.0.
Meaning that the values should be concentrated around 5.0, and rarely further away than 1.0 from the mean.
And as you can see from the histogram, most values are between 4.0 and 6.0, with a top at approximately 5.0.