Metadata-Version: 2.1
Name: benfordslaw
Version: 0.1.2
Summary: benfordslaw is to test if an empirical (observed) distribution differs significantly from a theoretical (expected, Benfords) distribution.
Home-page: https://github.com/erdogant/benfordslaw
Author: Erdogan Taskesen
Author-email: erdogant@gmail.com
License: UNKNOWN
Download-URL: https://github.com/erdogant/benfordslaw/archive/0.1.2.tar.gz
Description: # benfordslaw
        
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        * benfordslaw is Python package to test if an empirical (observed) distribution differs significantly from a theoretical (expected, Benfords) distribution. The law states that in many naturally occurring collections of numbers, the leading significant digit is likely to be small. This method can be used if you want to test whether your set of numbers may be artificial (or manupilated). If a certain set of values follows Benford's Law then model's for the corresponding predicted values should also follow Benford's Law. Normal data (Unmanipulated) does trend with Benford's Law, whereas Manipulated or fraudulent data does not.
        
        * Assumptions of the data:
          1. The numbers need to be random and not assigned, with no imposed minimums or maximums.
          2. The numbers should cover several orders of magnitude
          3. Dataset should preferably cover at least 1000 samples. Though Benfordâ€™s law has been shown to hold true for datasets containing as few as 50 numbers.
        
        ### Contents
        - [Installation](#-installation)
        - [Requirements](#-Requirements)
        - [Quick Start](#-quick-start)
        - [Contribute](#-contribute)
        - [Citation](#-citation)
        - [Maintainers](#-maintainers)
        - [License](#-copyright)
        
        ### Installation
        * Install benfordslaw from PyPI (recommended). benfordslaw is compatible with Python 3.6+ and runs on Linux, MacOS X and Windows. 
        * It is distributed under the MIT license.
        
        ### Requirements
        ```python
        pip install -r requirements
        ```
        
        #### Quick Start
        ```
        pip install benfordslaw
        ```
        
        * Alternatively, install benfordslaw from the GitHub source:
        ```bash
        git clone https://github.com/erdogant/benfordslaw.git
        cd benfordslaw
        python setup.py install
        ```  
        
        #### Import benfordslaw package
        ```python
        import benfordslaw as bl
        
        # Load elections example
        df = bl.import_example()
        
        # Extract election information.
        X = df['votes'].loc[df['candidate']=='Donald Trump'].values
        
        # Print
        print(X)
        # array([ 5387, 23618,  1710, ...,    16,    21,     0], dtype=int64)
        
        # Make fit
        out = bl.fit(X)
        
        # Plot
        bl.plot(out, title='Donald Trump')
        ```
        <p align="center">
          <img src="https://github.com/erdogant/benfordslaw/blob/master/docs/figs/fig1.png" width="600" />
        </p>
        
        #### Citation
        Please cite benfordslaw in your publications if this is useful for your research. Here is an example BibTeX entry:
        ```BibTeX
        @misc{erdogant2020benfordslaw,
          title={benfordslaw},
          author={Erdogan Taskesen},
          year={2019},
          howpublished={\url{https://github.com/erdogant/benfordslaw}},
        }
        ```
        
        #### References
        * https://en.wikipedia.org/wiki/Benford%27s_law
        * https://towardsdatascience.com/frawd-detection-using-benfords-law-python-code-9db8db474cf8
           
        #### Maintainers
        * Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
        
        #### Contribute
        * Contributions are welcome.
        
        #### Licence
        See [LICENSE](LICENSE) for details.
        
        #### Donation
        * This work is created and maintained in my free time. Contributions of any kind are very appreciated. <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Sponsering</a> is also possible.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
