PyPi package for modelling Probability distributions
probdists is a Python package to model probability distributions.
Supports Bernoulli, Binomial, Exponential, Gamma, Gaussian, Triangular, and Uniform Distributions with support for Poisson, Bates, and Irwin-Hall on the way!
For usage, see here.
Installing using pip:
>>> pip install probdists
Installing using virtual-environment (recommended):
>>> conda create -n myEnv python=3.6 anaconda
>>> conda activate myEnv
>>> pip install probdists
You can find the project on PyPi here.
Here, we shall demonstrate a general workflow by taking a particular class, say, Gaussian.
After installation, we can likewise interact with the package:
>>> from probdists import Gaussian
>>> gaussian = Gaussian()
>>> gaussian.read_data_file('demo_gaussian_data')
# for your own file, replace 'demo_gaussian_data' with 'my_text_file.txt'
# to access data
>>> print(gaussian.data)
[1, 3, 99, 100, 120, 32, 330, 23, 76, 44, 31]
# to calculate mean
>>> print(gaussian.calculate_mean())
78.09
# to calculate standard deviation
>>> print(gaussian.calculate_stdev())
92.87
# to calculate pdf
>>> print(gaussian.calculate_pdf(25, 5))
0.00365
# to calculate cdf
>>> print(gaussian.cdf(25, 5))
0.28378
# to add two individual distributions
>>> gaussian_one = Gaussian(25, 3)
>>> gaussian_two = Gaussian(30, 4)
>>> gaussian_sum = gaussian_one + gaussian_two
>>> print(gaussian_sum.mean)
55
>>> print(gaussian_sum.stdev)
5.0
# plot histogram of data
>>> gaussian.plot_histogram()
# plot normalized histogram of data and plot of pdf along same range
>>> gaussian.plot_histogram_pdf(n_spaces = 50)
The Distribution class is a base class that all other concrete classes inherit from. It provides common fields such as mean, stdev, pdf, and cdf and implements read_data_file() function.
read_data_file(self, file_name, separator='\\n', header=None)Function to read in data from a txt file, csv file and excel formats (xls, xlsx, xlsm, xlsb, odf, ods and odt)
The txt file should have one number (float) per line or numbers should be separator seperated.
No need for separator argument with csv file, it will by default be ‘,’ so csv files should have , seperated numbers
For excel file formats. There should only be one column containing numbers, and if 0th row is header then header argument should be 0. The numbers are taken from next row mentioned in header parameter.
The numbers are stored in the data attribute.
Args:
file_name (string): name of a file to read from
separator (character): custom separator to use if required
header (int or by default None): to specify if excel file
contains header.
Returns:
None
You should instantiate a child of Distribution class and not this class itself.
Bernoulli distribution class for calculating and visualizing a Bernoulli distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing the standard deviation of the distribution
data_list (list of floats) to be extracted from the data file
p (float) representing the probability of an event occurring (1). Default 0.5
` init(self, prob=0.5)`
Constructor function for Bernoulli class.
Args:
p (float): representing the probability of an event occurring (1). Default 0.5
calculate_mean(self, round_to=2)
Method to calculate the mean of a Bernoulli distribution
Args:
round_to (int): Round the mean value. Defaults to 2.
Returns:
float: mean of the data set
calculate_stdev(self, round_to=2)
Function to calculate the standard deviation from p.
Args:
round_to (int): Round the mean value. Defaults to 2.
Returns:
float: standard deviation of the data set.
replace_stats_with_data(self)Method to calculate p from the data set
Args:
None
Returns:
float: the p value
plot_bar(self)
Method to plot a histogram of the instance variable data using matplotlib pyplot library.
Args:
None
Returns:
None
calculate_pdf(self, k, round_to=2)Method to calculate pdf for the bernoulli distribution.
Args:
k (float): point for calculating the probability density function. Range of k: {0, 1}
round_to (int): Round the mean value. [Default value: 2 floating point]
Returns:
float: probability density function output
calculate_cdf(self, k, round_to=2)Method to calculate cdf for the bernoulli distribution.
Args:
k (float): point for calculating the cumulative distribution function
round_to (int): Round the mean value. [Default value: 2 floating point]
Returns:
float: cumulative distribution function output
plot_bar_pdf(self)Method to plot the pdf of the bernoulli distribution
Args:
None
Returns:
list: x values for the pdf plot
list: y values for the pdf plot
The Bernoulli class overrides __add__() and __repr__() functions to allow you to
add two objects and get characteristic about it, respectively.
Binomial distribution class for calculating and visualizing a Binomial distribution.
Attributes:
mean (float) representing the mean value of the distribution
stdev (float) representing the standard deviation of the distribution
data_list (list of floats) to be extracted from the data file
p (float) representing the probability of an event occurring
n (int) number of trials
__init__(self, prob=.5, size=20)
Constructor function for Binomial Distribution.
calculate_mean(self, round_to=2)
Function to calculate the mean from p and n
Args:
round_to (int): Round the mean value. [Default value: 2 floating point]
Returns:
float: mean of the data set
calculate_stdev(self, round_to=2)
Function to calculate the standard deviation from p and n.
Args:
round_to (int): Round the mean value. [Default value: 2 floating point]
Returns:
float: standard deviation of the data set
replace_stats_with_data(self)
Function to calculate p and n from the data set
Args:
None
Returns:
float: the p value
float: the n value
plot_bar(self)
Function to output a histogram of the instance variable data using matplotlib pyplot library.
Args:
None
Returns:
None
calculate_pdf(self, k, round_to=2)
Probability density function calculator for the binomial distribution.
Args:
k (float): point for calculating the probability density function
round_to (int): Round the mean value. [Default value: 2 floating point]
Returns:
float: probability density function output
calculate_cdf(self, k, round_to=2)
Cumulative distribution function calculator for the binomial distribution.
Args:
k (float): point for calculating the cumulative distribution function
round_to (int): Round the mean value. [Default value: 2 floating point]
Returns:
float: cumulative distribution function output
plot_bar_pdf(self)
Function to plot the pdf of the binomial distribution
Args:
None
Returns:
list: x values for the pdf plot
list: y values for the pdf plot
The Binomial class overrides __add__() and __repr__() functions to allow you to
add two objects and get characteristic about it, respectively.
Please find the code repository for probdists here.
probdists is distributed under MIT License.