One of the best ways to describe a variable is to report the values that appear in the dataset and how many times each value appears. This description is called the distribution of the variable.

The most common representation of a distribution is a histogram, which is a graph that shows the frequency of each value. In this context, “frequency” means the number of times the value appears.

An example of histogram.

See more at Thinkstats2, Chapter 02: Distribution

Another way to represent a distribution is a **probability mass function (PMF)**, which maps from each value to its **probability**. A **probability** is a frequency expressed as a fraction of the sample size, n. To get from frequencies to probabilities, we divide through by n, which is called **normalization**.

Pmf and Hist objects are similar in many ways; in fact, they inherit many of their methods from a common parent class. For example, the methods Values and Items work the same way for both. The biggest difference is that a Hist maps from values to integer counters; a Pmf maps from values to floating-point probabilities.

See more at Thinkstats2, Chapter03: PMF