addresses

lovelyrita.addresses.clean_street_suffixes(street)

lovelyrita.addresses.parse_123_main_street(addresses)

Parse the common address format, e.g. 123 MAIN STREET

Returns:

A DataFrame containing street name and street column for those rows that were successfully

parsed

lovelyrita.addresses.parse_P123_main_street(addresses)

Parse addresses that contain a prefix, e.g., P123-1 PARK STREET

Returns:

A DataFrame containing street name and street column for those rows that were successfully

parsed

lovelyrita.addresses.parse_addresses(addresses)

Parse addresses into street name and number according to several rules.

Returns:

A DataFrame containing street name and street column for those rows that were successfully

parsed

lovelyrita.addresses.replace(addresses, replacements=[('^ONE ', '1 '), ('^TWO ', '2 '), (' -', '-'), (' TERM$', ' TERMINAL'), ('^#', '')], inplace=True)

Replace text in addresses

Parameters:

addresses : pandas.Series

replacements : tuple list of tuples

Replacements provided as (pattern to replace, replacement). Multiple replacements can be provided as a list of tuples

inplace : bool

Returns:

If inplace is False, returns the address series with the replacements made.