As part of the Udacity Data Science nanodegree this is a project to create and deploy a Python package, based on this I decided to use the Benford´s Law to create a package using OOP than can apply the analyzis to a dataset, an imgae or a numpy array of numbers.

pip intall benford_law

This package uses these libraries

• pandas
• numpy
• matplotlib

The python version used: 3.8

• README.md : This file
• setup.py : Setup file for the package
• .gitignore : File to ignore files and not load to the GitHub repository
• LICENSE : The MIT License
• benford_law
  • bendord_law.py : The Benford Class
  • init.py : Inititalization file for the package
• tests
  • tests.py : Script for unit tests of the Benford Class
  • Benford_plot_test.ipynb : Jupyter Notebook to test the graphs plotting ot the Benford Class
  • flower.jpg : JPG image to test the Benford Class
  • population.csv : CSV dataset with the Wordl Population to test the Benford Class

1. Create an instace with the Benford Class

   • benford = Benford()

2. Load a dataset or an image ,

   • bendford.load_dataset(dataset_name)
   • benford.load_image(image_name)

3. Do the benford analysis using the benford_analysis method

   • benford.benford_analysis()

4. Graph or plot the results using plot() method

   • benford.plot()

5. Save the results using expor_to_csv() method:

   • benford.export_to_csv(filename)

#Import libraries
import pandas as pd
import numpy as np
from benford_law import Benford
import matplotlib.pyplot as plt
from PIL import Image


image_file = 'flower.jpg'      #Read image
image = Benford()              #Create Benford instance
image.load_image(image_file)   #Load image
image.benford_analysis()       #Do Analysis
image.plot()                   #Plot Benford´s analysis

Apply the Benford´s Law analysis to this image:

This is a tool to do an initial analyzis using Benford´s Law and plot how the digit´s probability are distributed on different datasets, it is not a formal analysis or study using the Benford´s Law

The code follows this license: https://opensource.org/licenses/MIT