1. Absolute frequency:
This is the number of observations in a particular category. It always has an integer value, or we can say that it has discrete values.
Following data are given about pass or fail of students in an exam held of Mathematics in a class.
P, P, F, P, F, P, P, F, F, P, P, P
where, P = Passed and F = Failed.
From the given data we can say that,
There are 8 students who passed the exam
There are 4 students who failed the exam
Implementation in Python:
Let the result be 12 people declared in two categories Pass (P) and Fail (F), classified as 1 and 0 respectively.
P, P, F, P, F, P, P, F, F, P, P, P 1 , 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1
1 8 0 4 dtype: int64
2. Relative frequency:
This is the proportion of observations of a specific category in this dataset. It has floating values and is also represented as a percentage. Consider the following example of passed and failed students on a math exam. Then,
relative frequency of passed students = 8 / (8 + 4) = 0.666 = 66.6%
relative frequency of failed students = 4 / (8 + 4) = 0.333 = 33.3%
1 0.666667 0 0.333333 dtype: float64