NumPy Tutorial · NumPy Random

Data Distribution

Learn all about Data Distribution in this comprehensive tutorial.

5 min read advanced

•Data Distribution is a list of all possible values, and how often each value occurs.
•A random distribution is a set of random numbers that follow a certain probability density function.

What is Data Distribution?

Data Distribution is a list of all possible values, and how often each value occurs.

Such lists are important when working with statistics and data science.

The random module offer methods that returns randomly generated data distributions.

A random distribution is a set of random numbers that follow a certain probability density function.

Note: Probability Density Function: A function that describes a continuous probability. i.e. probability of all values in an array.

We can generate random numbers based on defined probabilities using the choice() method of the random module.

The choice() method allows us to specify the probability for each value.

The probability is set by a number between 0 and 1, where 0 means that the value will never occur and 1 means that the value will always occur.

python

from numpy import randomx = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 
  0.6, 0.0], size=(100))print(val_1)

Note: The sum of all probability numbers should be 1.

Even if you run the example above 100 times, the value 9 will never occur.

You can return arrays of any shape and size by specifying the shape in the size parameter.

python

from numpy import randomx = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 
  0.6, 0.0], size=(3, 5))print(val_1)

2 questions

Which of the following is true about Data Distribution?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with Data Distribution?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.