Probabilistic Functional

This repository has the purpose of implement probabilistic and statistical functions using concepts of functional programming at python. The book used as support material is: DOWNING, Douglas; CLARK, Jeffrey. Estatística aplicada. 3. ed. São Paulo: Saraiva, 2010.

Requeriments

Setup

$ pip install -r requeriments.txt

Methods

The struct of this section is:

Some explanation / math definitions
# Params
methodSyntax()
# Return

getData

# file_name: path of csv file you want to get data
# index: column to extract data
getData(file_name, index)
# Return a Tuple with all data of csv file and column passed as arguments 

average

Value that shows where the values of collection are concentrates.

# vector: data vector to extract the average
average(vector)
# Return a number that represents the average of a collection

median

For a relation where the collections values are sorted, the median is the number that has many values above as below of him.

# vector: data vector to extract the median
median(vector)
# Return a number that represents the median of a collection

mode

Value that occurs more often at a collection.

# vector: data vector to extract the mode
mode(vector)
# Return a number that represents the mode of a collection

amplitude

Represents the distance between the higher and lower value.

# vector: data vector to extract the amplitude
mode(vector)
# Return a number that represents the amplitude of a collection

absoluteMeanDeviation

Mean of distances between each value and the mean.

# vector: data vector to extract the absoluteMeanDeviation
absoluteMeanDeviation(vector)
# Return a number that represents the absoluteMeanDeviation of a collection

variance

Absolute Mean Deviation squared.

# vector: data vector to extract the variance
variance(vector)
# Return a number that represents the variance of a collection

standardDeviation

Square root of Variance.

# vector: data vector to extract the standardDeviation
standardDeviation(vector)
# Return a number that represents the standardDeviation of a collection

coefficientVariation

Measures the dispersion in relation to the mean.

# vector: data vector to extract the coefficientVariation
coefficientVariation(vector)
# Return a number that represents the coefficientVariation of a collection