In statistics a **distribution** is a
set of values and their corresponding probabilities.

For example, if you roll a six-sided die, the set of possible values is the numbers 1 to 6, and the probability associated with each value is 1/6.

As another example, you might be interested in how many times each word appears in common English usage. You could build a distribution that includes each word and how many times it appears.

To represent a distribution in Python, you could use a dictionary
that maps from each value to its probability. I have written a class
called `Pmf`

that uses a Python
dictionary in exactly that way, and provides a number of useful methods. I
called the class Pmf in reference to a **probability
mass function**, which is a way to represent a distribution
mathematically.

`Pmf`

is defined in a Python module
I wrote to accompany this book, `thinkbayes.py`

. You can download it from http://thinkbayes.com/thinkbayes.py.
For more information see Working with the code.

To use `Pmf`

you can import it like
this:

from thinkbayes import Pmf

The following code builds a Pmf to represent the distribution of outcomes for a six-sided die:

pmf = Pmf() for x in [1,2,3,4,5,6]: pmf.Set(x, 1/6.0)

`Pmf`

creates an empty
Pmf with no values. The `Set`

method sets the probability associated with
each value to .

Here’s another example that counts the number of times each ...

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