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.
Example :
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.
Solution:
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
import
pandas as pd
data
=
[
1
,
1
,
0
,
1
,
0
,
1
,
1
,
0
,
0
,
1
,
1
,
1
]
# Create a data frame with using the pandas library
# .value_counts () counts the number
# specific observation cases
df
=
pd.Series (data) .value_counts ()
print
(df)
Exit :
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%

Exit:
1 0.666667 0 0.333333 dtype: float64