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Pyap is an MIT Licensed text processing library, written in Python, for detecting and parsing addresses. Currently it supports USA, Canadian and British addresses.

Project description

Pyap is an MIT Licensed text processing library, written in Python, for detecting and parsing addresses. Currently it supports US 🇺🇸, Canadian 🇨🇦 and British 🇬🇧 addresses.

>>> import pyap
>>> test_address = """
    Lorem ipsum
    225 E. John Carpenter Freeway,
    Suite 1500 Irving, Texas 75062
    Dorem sit amet
    """
>>> addresses = pyap.parse(test_address, country='US')
>>> for address in addresses:
        # shows found address
        print(address)
        # shows address parts
        print(address.as_dict())
...

Installation

To install Pyap, simply:

$ pip install pyap

About

This library has been created because i couldn’t find any reliable and opensource solution for detecting addresses on web pages when writing my web crawler. Currently available solutions have drawbacks when it comes to using them to process really large amounts of data fast. You’ll either have to buy some proprietary software; use third-party pay-per-use services or use address detecting which is slow and unsuitable for real-time processing.

Pyap is an alternative to all these methods. It is really fast because it is based on using regular expressions and it allows to find addresses in text in real time with low error rates.

Future work

  • Add rules for parsing FR addresses

Typical workflow

Pyap should be used as a first thing when you need to detect an address inside a text when you don’t know for sure whether the text contains addresses or not.

To achieve the most accuracy Pyap results could be reverified using geocoding process.

Limitations

Because Pyap is based on regular expressions it provides fast results. This is also a limitation because regexps intentionally do not use too much context to detect an address.

In other words in order to detect US address, the library doesn’t use any list of US cities or a list of typical street names. It looks for a pattern which is most likely to be an address.

For example the string below would be detected as a valid address: “1 SPIRITUAL HEALER DR SHARIF NSAMBU SPECIALISING IN”

It happens because this string has all the components of a valid address: street number “1”, street name “SPIRITUAL HEALER” followed by a street identifier “DR” (Drive), city “SHARIF NSAMBU SPECIALISING” and a state name abbreviation “IN” (Indiana).

The good news is that the above mentioned errors are quite rare.

Project details


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