Python package for calculating and visualizing Gaussian and Binomial distributions. The package contains methods for performing computations pertaining to Guassian and Binomial distributions.
Link to the PyPI project: https://pypi.org/project/gauss-binomial-mp99/
$ pip install gauss-binomial-mp99
The parent class, contains __init__
and read_data_file
methods.
Import:
>>> from gauss_binomial_mp99 import Gaussian, Binomial
>>> gauss = Gaussian()
>>> gauss.read_data_file('sample.txt')
Gaussian distribution class for calculating and visualizing a Gaussian distribution. Methods to compute Gaussian distribution features include-
calculate_mean()
: Function to calculate the mean of the data set.calculate_stdev()
: Function to calculate the standard deviation of the data set.plot_histogram()
: Function to output a histogram of the instance variable data using matplotlib pyplot library.pdf(x)
: Probability density function calculator for the gaussian distribution.plot_histogram_pdf
: Function to plot the normalized histogram of the data and a plot of the probability density function along the same range
Default: mu= 0 and sigma=1 Form: guass(mu, sigma)
Example:
>>> gauss.calculate_mean()
78.0909090909091
>>> gauss.calculate_stdev()
92.87459776004906
Provides the functionality of adding two Gaussian distribution objects.
Binomial distribution class for calculating and visualizing a Binomial distribution. Methods to compute Gaussian distribution features include-
calculate_mean()
: Function to calculate the mean from p= probability and n= sizecalculate_stdev()
: Function to calculate the standard deviation from p and n.plot_bar()
: Function to output a histogram of the instance variable data using matplotlib pyplot library.pdf(x)
: Probability density function calculator for the gaussian distribution.plot_bar_pdf
: Function to plot the pdf of the binomial distribution.
Default: p= 0.5 and n=20 Form: bin(p, n)
>>> bin= Binomial(.45, 78)
>>> bin.pdf(34)
0.08798942891783665
Provides the functionality of adding two Binomial distribution objects with the same probablity.