Airtable, Python and Fuzzy Matching
Inspired by this question:
I thought I would try to use Python to do some fuzzy matching on Airtable data. Not really an Airtable demo (as the data could be stored anywhere), but hopefully some readers find this interesting :slightly_smiling_face:
This article:
pointed me to a Python library for fuzzy matching (fuzzywuzzy) and gives some good code snippets for how to use the library.
As per the original post, I used a Donors/Donation base to prototype the matching process. I have a Donors table:
and a Donations table:
The aim is to use a fuzzy matching routine to suggest which is the closest matching record in the Donor table for each Donation.
I added Suggested Donor and Suggested Donor Score fields to the Donations table:
I have a Python script which uses the Airtable API to compare every donation name to every donor name and insert the closest matching, i.e. highest scoring, into the Suggested Donor field. This is set up as a linked record so that, for example, the donation total could be rolled up on the Donors table.
When the script runs you end up with this:
(A score of 100 being an exact match). There’s some low scores (58) which are obviously not a match and some high scores (James Smith/Jane Smith/86) which are also not a match.
Setting the threshold to a high number in the script, e.g. >= 95, gives this:
This might be a better approach where a decent proportion of the records are matched, leaving you to match the others manually, but you have high confidence in the records that have been matched.
Here’s the Python script:
import json
import requests
from fuzzywuzzy import process
get_headers = {
'Authorization': 'Bearer YOUR_API_KEY'
}
# get an array of donors
donors_url = 'https://api.airtable.com/v0/YOUR_APP_ID/Donors?maxRecords=100&view=All_Donors'
donors_response = requests.get(donors_url, headers=get_headers)
donors_data = donors_response.json()
donors_list = []
for j in donors_data['records']:
donors_list.append(j['fields']['Name'])
# get the donations
donations_url = 'https://api.airtable.com/v0/YOUR_APP_ID/Donations?maxRecords=100&view=All_Donations'
donations_response = requests.get(donations_url, headers=get_headers)
donations_data = donations_response.json()
# for each donation get the highest matching donor record
for i in donations_data['records']:
donor_name = i['fields']['Name']
highest = process.extractOne(donor_name, donors_list)
# if the highest match has a score >= 80 (or some other score)...
if highest[1] >= 95:
# ...get the donor record...
params = querystring = {"filterByFormula":"({Name}=\""+highest[0]+"\")"}
get_matching_donor_response = requests.get(donors_url, headers=get_headers, params=querystring)
matching_donor_id = get_matching_donor_response.json()['records'][0]['id']
# ... and update the donation with the "suggested donor"
update_url = 'https://api.airtable.com/v0/YOUR_APP_ID/Donations/' + i['id']
update_headers = {
'Authorization': 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json'
}
update_data = {
"fields": {
"Suggested Donor": [
matching_donor_id
],
"Suggested Donor Score": highest[1]
}
}
print(update_data)
update_response = requests.patch(update_url, headers=update_headers, json=update_data)
print(update_response.text)
Looking forward to the new Airtable scripting capability (now in private beta?) where I hope we might be able to do similar things wholly within Airtable.
JB
@Matteo_Cossu2 - this is a bit embarrasing…I’d actually written a script for this, but had totally forgotten about it. I cribbed some “distance algorithm” code from the web somewhere and incorporated it into an Airtable script:
// Levenshtein distance function
let levenshteinDistance = function(a, b){
if(!a || !b) return (a || b).length;
var m = [];
for(var i = 0; i <= b.length; i++){
m[i] = [i];
if(i === 0) continue;
for(var j = 0; j <= a.length; j++){
m[0][j] = j;
if(j === 0) continue;
m[i][j] = b.charAt(i - 1) == a.charAt(j - 1) ? m[i - 1][j - 1] : Math.min(
m[i-1][j-1] + 1,
m[i][j-1] + 1,
m[i-1][j] + 1
);
}
}
return m[b.length][a.length];
};
// max string length function (gets the max length of two strings)
let maxStringLength = function(a, b){
return Math.max(a.length, b.length);
};
//get the ratio that the user wants to use on this iteration, e.g. 0.8
let inputRatio = await input.textAsync("Enter the match ratio for this search (number between 0 and 1)")
// set the tables
let matchTable = base.getTable("Match");
let originalTable = base.getTable("Original");
// get the records from the master and match tables
let matchQuery = await matchTable.selectRecordsAsync({
fields: ['Name']
});
let originalQuery = await originalTable.selectRecordsAsync({
fields: ['Name']
});
// iterate through the match records
for (let matchRecord of matchQuery.records) {
// create an empty array to hold the matched IDs from master
let matchedIds = []
// iterate through the master records
for (let masterRecord of originalQuery.records) {
// do some regex so that only leters and numbers are considered - also lower case the two strings
let formattedMatchRecord = matchRecord.name.toLowerCase().replace(/[^a-z0-9]/gi,'');
let formattedOriginalRecord = masterRecord.name.toLowerCase().replace(/[^a-z0-9]/gi,'');
// calculate the Levenshtein distance
let ld = levenshteinDistance(formattedMatchRecord, formattedOriginalRecord);
// calculate the max length of each pair of strings (match and master)
let maxLength = maxStringLength(formattedMatchRecord, formattedMatchRecord);
// log the inputs and outputs for debugging
console.log(formattedMatchRecord, formattedOriginalRecord, ld, maxLength, 1-ld/maxLength);
// if the calculated ratio is greater than the input threshold
if(1-ld/maxLength > Number(inputRatio)) {
// add the master id to the array
matchedIds.push(
{id: masterRecord.id}
)
}
}
// update the match record with the array of master ids
let update = await matchTable.updateRecordAsync(matchRecord, {
"Suggested Link to Original": matchedIds
}
);
}
The way this works is that you have 2 tables, both with a name column and the script links to suggested records based on the “closeness” of the original name to the match name.
You enter a number between 0 and 1 at the start (try 0.7 or 0.8) - the higher the number the more strict the threshold for suggesting matches. More info here:
The script implements the Levenshtein distance algorithm to check for similarity in two strings. This allows you to suggest links between two sets of data based on the similarity of fields within the base. This might be useful, for example, where you have a “original” customer list and want to try to match or link that to a second source of customer data.
More information on the Levenshtein distance can be found here: Levenshtein distance - Wikipedia
