dg-probability 1.0

Gaussian and Binomial distributions

Gaussian and Binomial Distributions

This package contains code to perform basic mathematical operations on Gaussian and Binomial distributions. Operations include finding the mean and standard deviation (of both sample and population), plotting histograms for Gaussian distribution and plotting frequncy bar charts for Binomial distributions.

Dummy input data

For Gaussian distribution, you can test the package using the following dummy input data.

1
3
99
100
120
32
330
23
76
44
31

Here's some dummy data for a Binomial distribution. It contains the outcomes of 13 trials. A '0' denotes failure and a '1' denotes success.

0
1
1
1
1
1
0
1
0
1
0
1
0

Test the code

Copy the following code snippet in a .py file and execute it.

from dg_probability import Gaussian

gaussian = Gaussian(10,5)
print(f"Gaussian mean = {gaussian.mean}")
print(f"Gaussian standard deviation = {gaussian.stdev}")

This snippet is for the Binomial module.

from dg_probability import Binomial

binomial = Binomial(0.25,60)
print(f"Binomian mean = {binomial.mean}")
print(f"Binomian standard deviation = {binomial.stdev}")

Test the entire Binomial distribution module

Here's how you can test the entire Binomial distribution module using the dummy data shown in the 'Input data' section. Create a file called data_binomial.txt and add the dummy data for Binomial distribution to it. Save this file in the same directory where your code is.

# ignore if module is already imported
from dg_probability import Binomial

binomial = Binomial()
binomial.read_data_file('data_binomial.txt')
binomial.calculate_mean()
binomial.calculate_stdev()
binomial.replace_stats_with_data()
binomial.plot_bar()
binomial.plot_bar_pdf()

The methods for Gaussian distribution are similar.

Contributions

There is not CONTRIBUTIONS.md file yet but you are welcome to create PRs :)

Source code

https://github.com/dg1223/object-oriented-programming/tree/main/upload_to_pypi