Postcodes

These functions are designed to be run on postodes.

Format Postode

This function takes a postcode, attempts to clean and format it in accordance to UK standards, and validate that it is a valid postcode form. The validation logic matches formats: A9 9AA, A99 9AA, AA9 9AA, AA99 9AA, A9A 9AA, AA9A 9AA. Postcodes will be returned with all uppercase letters, and a space between the outward and inward part.

Errors will be raised if postcode is not a valid string (text) or if the format does not conform to the UK standard. You can ignore errors with errors='ignore' which will return the original postcode. You can also 'coerce' errors which will attempt to turn postcode into a string. If this still fails and raises an error, None will be returned.

Warning

This function does not validate that a UK postcode exists, only that it conforms to the expected format.

Example with list of postcodesExample on pandas dataframe

import heat_helper as hh

postcode_messy = ['aa11aa', 'w1a7nn', 'ST1 1AA', 'ST1', 'st5   5BG']

clean_postcodes = [hh.format_postcode(postcode, errors='ignore') for 
                    postcode in postcode_messy]

print(clean_postcodes)

#Output: ['AA1 1AA', 'W1A 7NN', 'ST1 1AA', 'ST1', 'ST5 5BG']

import heat_helper as hh
import pandas as pd

postcode_messy = ['aa11aa', 'w1a7nn', 'ST1 1AA', 'ST1', 'st5   5BG']

postcode_dict = {'Postcodes' : postcode_messy}

postcode_df = pd.DataFrame(postcode_dict)

postcode_df['Clean Postcodes'] = postcode_df['Postcodes'].apply(
    hh.format_postcode, errors='coerce'
    )

print(postcode_df.head(5))

#Output:
#   Postcodes Clean Postcodes
#0     aa11aa         AA1 1AA
#2    ST1 1AA         ST1 1AA
#1     w1a7nn         W1A 7NN
#3        ST1            None
#4  st5   5BG         ST5 5BG