Absolute and relative frequency in pandas

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%

import pandas as pd   data = [ 1 , 1 , 0 , 1 , 0 , 1 , 1 , 0 , 0  , 1 , 1 , 1 ]   # Create a data frame using the pandas library # .value_counts () counts the number # specific observation cases   df = pd.Series (data) .value_counts ()  print (df / len (data))

Exit:

 1 0.666667 0 0.333333 dtype: float64