Absolute and Relative frequency in Pandas

Frequency is the number of occurrences of an outcome in the given sample. It can be termed in two different ways.

1. Absolute Frequency:
It is the number of observations in a particular category. It has always an integer value or we can say 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’s the result of 12 persons declared in two categories Pass(P) and Fail(F) is categorized 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 Data Frame using pandas library
# .value_counts() counts the number of
# occurrences of particular observation

df = pd.Series(data).value_counts()
print(df)

Output:

1    8
0    4
dtype: int64

2. Relative Frequency:
It is the fraction of observations of a particular category in given data set. It has floating values and also represented in percentage. Let us consider the given example of passed and failed students in the Mathematics 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 %

import pandas as pd

data = [1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1]

# Create Data Frame using pandas library
# .value_counts() counts the number of
# occurrences of particular observation

df = pd.Series(data).value_counts()
print(df / len(data))

Output:

1    0.666667
0    0.333333
dtype: float64