aliases 0.5.0

Keeping track of aliases

A very small Python package for keeping track of aliases.

Installation

$ pip install aliases

Getting Started

Keeping track of aliases in your data can be annoying. This small packages provides three small classes than can help you in the bookkeeping associated with the occurrences of aliases in your data.

The AliasSpace objects keeps track of existing aliases. As input is accepts a dictionary where a string (the “preferred” form) points to a list of all its aliases. Using the str method on the space, we can transform regular strings into AliasAwareString objects.

>>> s = AliasSpace(
>>>     {
>>>         "The Netherlands": ["NL", "Netherlands", "Holland"],
>>>         "The Hague": ["Den Haag", "'s-Gravenhage"],
>>>         "Amsterdam": ["Adam"],
>>>     },
>>>     case_sensitive=False,
>>> )
>>>
>>> s.str("nl")
<'nl' in AliasSpace>

The preferred form of an AliasAwareString is called its representative (because it represents the equivalance class of the string under the equivalance relation of being aliases).

>>> s.str("nl").representative
'The Netherlands'

AliasAwareString objects with the same representative are considered equal and have the same hash.

>>> s.str("holland") == s.str("NL")
True
>>>
>>> data = {s.str("holland"): 12345}
>>> data[s.str("nl")]
12345

The example above already shows how alias aware strings can be used to store data without worrying too much about the different aliases around. However, it is still annoying to cast to an AliasAwareString every time manually. To solve this you can use the AliasAwareDict. This object can be created using the dict method on the space.

>>> data = s.dict(holland=12345)
>>> data['nl']
12345

Finally, when you have pandas installed, the aliases package will register accessors for series and dataframes. This allows you to easily enforce aliases in your pandas DataFrame. The following example was the original motivation for building this package:

>>> import pandas as pd
>>> df = pd.DataFrame(
>>>     {
>>>         "Country": ["NL", "Netherlands", "Belgium"],
>>>         "City": ["Den Haag", "amsterdam", "Brussel"],
>>>         "SomeData": [10, 11, 12],
>>>     }
>>> )
       Country       City  SomeData
0           NL   Den Haag        10
1  Netherlands  amsterdam        11
2      Belgium    Brussel        12
>>>
>>> df.Country.alias.representative(space=s)
0    The Netherlands
1    The Netherlands
2            Belgium
Name: Country, dtype: object
>>>
>>> df.alias.representative(space=s, missing=pd.NA)
           Country       City  SomeData
0  The Netherlands  The Hague        10
1  The Netherlands  Amsterdam        11
2             <NA>       <NA>        12

Documentation

Coming soon…