agefromname 0.0.1

Predict how old someone is from their name and gender, using the baby name data from the US Social Security Administration.

# AgeFromName 0.0.1
A tool for predicting someone's age or generation given their name and assigned sex at birth,
assuming they were born in the US.

Feel free to use the Gitter community [gitter.im/agefromname](https://gitter.im/agefromname/Lobby) for help or to discuss the project.

## Installation

`$ pip install agefromname`

## Overview

This more or less apes the approach of FiveThirtyEight's ["How to Tell if Someone's Age
When All you Know Is Her Name"](https://fivethirtyeight.com/features/how-to-tell-someones-age-when-all-you-know-is-her-name/) article.

It includes data collected scraped from the Social Security
Administration's [Life Tables for the United States Social Security Area 1900-2100](https://www.ssa.gov/oact/NOTES/as120/LifeTables_Body.html#wp1168591)
and their [baby names data](http://www.ssa.gov/oact/babynames/names.zip). Code is included
to re-scrape and refresh this data in `regenerate_data.py`. It includes data as far back as
1981.

To use, first initialize the finder

```pydocstring
>>> from agefromname import AgeFromName
>>> age_from_name = AgeFromName()
```

Now you can use this to get the maximum likelihood estimate of someone's age, give their first name and
gender. Note that their gender should be a single letter, 'm' or 'f' (case-insensitive), and that the
first name is case-insensitive as well.

```pydocstring
>>> age_from_name.get_mle('jAsOn', 'm')
1977
>>> age_from_name.get_mle('Jason', 'M')
1977
```

You can also include find the MLE after as of a particular year. Note that if omitted, the current
year is used.

```pydocstring
>>> age_from_name.get_mle('john', 'm', 1980)
1947
>>> age_from_name.get_mle('john', 'm', 2000)
1964
```

Getting estimated counts of living people with a giving name and gender at a particular date is easy,
and given in a Pandas Series.
```pydocstring
>>> age_from_name.get_estimated_counts('john', 'm', 1960)
year_of_birth
1881 4613.792420
1882 5028.397099
1883 4679.560929
...
```

We can see corresponding probability distribution using

```pydocstring
>>> age_from_name.get_estimated_distribution('mary', 'f', 1910)
year_of_birth
1881 0.016531
1882 0.019468
1883 0.019143
...
```

Finally, we can see similar information for generations, as well, using the GenerationFromName class.
```pydocstring
>>> generation_from_name.get_mle('barack', 'm')
'Generation Z'
>>> generation_from_name.get_mle('ashley', 'f')
'Millenials'
>>> generation_from_name.get_mle('monica', 'f')
'Generation X'
>>> generation_from_name.get_mle('bill', 'm')
'Baby Boomers
>>> generation_from_name.get_mle('bill', 'f')
'Greatest Generation'
>>> generation_from_name.get_estimated_distribution('jaden', 'm')
Baby Boomers 0.000000
Generation X 0.001044
Generation Z 0.897662
Greatest Generation 0.000000
Millenials 0.101294
_other 0.000000
Name: estimate_percentage, dtype: float64
>>> generation_from_name.get_estimated_distribution('gertrude', 'f')
Baby Boomers 0.259619
Generation X 0.031956
Generation Z 0.009742
Greatest Generation 0.425293
Millenials 0.011412
_other 0.261979
>>> generation_from_name.get_estimated_counts('ashley', 'f')
Baby Boomers 702.481287
Generation X 29274.206090
Generation Z 141195.016621
Greatest Generation 34.998913
Millenials 652914.233604
_other 0.102625
Name: estimated_count, dtype: float64
```