benford-py 0.5.0
A library for testing data sets with Bendford's Law
License: BSD 3-Clause

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# Benford for Python

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**Citing**


If you find *Benford_py* useful in your research, please consider adding the following citation:

```bibtex
@misc{benford_py,
      author = {Marcel, Milcent},
      title = {{Benford_py: a Python Implementation of Benford's Law Tests}},
      year = {2017},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/milcent/benford_py}},
}
```

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`current version = 0.5.0`

### See [release notes](https://github.com/milcent/benford_py/releases/) for features in this and in older versions

### Python versions >= 3.6

### Installation

Benford_py is a package in PyPi, so you can install with pip:

`pip install benford_py`

or

`pip install benford-py`

Or you can cd into the site-packages subfolder of your python distribution (or environment) and git clone from there:

`git clone https://github.com/milcent/benford_py`

For a quick start, please go to the [Demo notebook](https://github.com/milcent/benford_py/blob/master/Demo.ipynb), in which I show examples on how to run the tests with the SPY (S&P 500 ETF) daily returns.

For more fine-grained details of the functions and classes, see the [docs](https://benford-py.readthedocs.io/en/latest/index.html).

### Background

The first digit of a number is [its leftmost digit](https://github.com/milcent/benford_py/blob/master/img/First_Digits.png)

Since the first digit of any number can range from "1" to "9"
(not considering "0"), it would be intuitively expected that the
proportion of each occurrence in a set of numerical records would
be uniformly distributed at 1/9, i.e., approximately 0.1111,
or 11.11%.

[Benford's Law](https://en.wikipedia.org/wiki/Benford%27s_law),
also known as the Law of First Digits or the Phenomenon of
Significant Digits, is the finding that the first digits of the
numbers found in series of records of the most varied sources do
not display a uniform distribution, but rather are arranged in such
a way that the digit "1" is the most frequent, followed by "2",
"3", and so in a successive and decremental way down to "9", 
which presents the lowest frequency as the first digit.

The expected distributions of the First Digits in a
Benford-compliant data set are the ones shown [here](https://github.com/milcent/benford_py/blob/master/img/First.png)

The first record on the subject dates from 1881, in the work of
[Simon Newcomb](https://github.com/milcent/benford_py/blob/master/img/Simon_Newcomb_APS.jpg), an American-Canadian astronomer and mathematician,
who noted that in the logarithmic tables the first pages, which
contained logarithms beginning with the numerals "1" and "2",
were more worn out, that is, more consulted.

In that same article, Newcomb proposed the [formula](https://github.com/milcent/benford_py/blob/master/img/formula.png) for the probability of a certain digit "d" 
being the first digit of a number, given by the following equation.

In 1938, the American physicist [Frank Benford](https://github.com/milcent/benford_py/blob/master/img/2429_Benford-Frank.jpg) revisited the 
phenomenon, which he called the "Law of Anomalous Numbers," in 
a survey with more than 20,000 observations of empirical data 
compiled from various sources, ranging from areas of rivers to
molecular weights of chemical compounds, including cost data, 
address numbers, population sizes and physical constants. All 
of them, to a greater or lesser extent, followed such 
distribution.

The extent of Benford's work seems to have been one good reason 
for the phenomenon to be popularized with his name, though 
described by Newcomb 57 years earlier.

Derivations of the original formula were also applied in the 
expected findings of the proportions of digits in other 
positions in the number, as in the case of the second digit
(BENFORD, 1938), as well as combinations, such as the first 
two digits of a number (NIGRINI, 2012, p.5).

Only in 1995, however, was the phenomenon proven by Hill. 
His proof was based on the fact that numbers in data series
following the Benford Law are, in effect, "second generation"
distributions, ie combinations of other distributions.
The union of randomly drawn samples from various distributions
forms a distribution that respects Benford's Law (HILL, 1995).

When grouped in ascending order, data that obey Benford's Law 
must approximate a geometric sequence (NIGRINI, 2012, page 21).
From this it follows that the logarithms of this ordered series
must form a straight line. In addition, the mantissas (decimal
parts) of the logarithms of these numbers must be uniformly
distributed in the interval [0,1] (NIGRINI, 2012, p.10).

In general, a series of numerical records follows Benford's Law
when (NIGRINI, 2012, p.21):
* it represents magnitudes of events or events, such as populations
of cities, flows of water in rivers or sizes of celestial bodies;
* it does not have pre-established minimum or maximum limits;
* it is not made up of numbers used as identifiers, such as 
identity or social security numbers, bank accounts, telephone numbers; and
* its mean is less than the median, and the data is not
concentrated around the mean.

It follows from this expected distribution that, if the set of
numbers in a series of records that usually respects the Law
shows a deviation in the proportions found, there may be
distortions, whether intentional or not.

Benford's Law has been used in [several fields](http://www.benfordonline.net/). 
Afer asserting that the usual data type is Benford-compliant,
one can study samples from the same data type tin search of
inconsistencies, errors or even [fraud](https://www.amazon.com.br/Benfords-Law-Applications-Accounting-Detection/dp/1118152859).

This open source module is an attempt to facilitate the 
performance of Benford's Law-related tests by people using
Python, whether interactively or in an automated, scripting way.

It uses the versatility of numpy and pandas, along with
matplotlib for vizualization, to deliver results like [this one](https://github.com/milcent/benford_py/blob/master/img/SPY-f2d-conf_level-95.png) and much more.


It has been a long time since I last tested it in Python 2. The death clock has stopped ticking, so officially it is for Python 3 now. It should work on Linux, Windows and Mac, but please file a bug report if you run into some trouble.

Also, if you have some nice data set that we can run these tests on, let'us try it.

Thanks!

Milcent