# 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 `

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