How to set a column value by fuzzy string matching with another dataframe?

Question

I have referred to this post but cannot get it to run for my particular case. I have two dataframes:

import pandas as pd

df1 = pd.DataFrame(
    {
        "ein": {0: 1001, 1: 1500, 2: 3000},
        "ein_name": {0: "H for Humanity", 1: "Labor Union", 2: "Something something"},
        "lname": {0: "Cooper", 1: "Cruise", 2: "Pitt"},
        "fname": {0: "Bradley", 1: "Thomas", 2: "Brad"},
    }
)

df2 = pd.DataFrame(
    {
        "lname": {0: "Couper", 1: "Cruise", 2: "Pit"},
        "fname": {0: "Brad", 1: "Tom", 2: "Brad"},
        "score": {0: 3, 1: 3.5, 2: 4},
    }
)

Then I do:

from fuzzywuzzy import fuzz
from fuzzywuzzy import process
from itertools import product

N = 60
names = {
    tup: fuzz.ratio(*tup)
    for tup in product(df1["lname"].tolist(), df2["lname"].tolist())
}

s1 = pd.Series(names)
s1 = s1[s1 > N]
s1 = s1[s1.groupby(level=0).idxmax()]

degrees = {
    tup: fuzz.ratio(*tup)
    for tup in product(df1["fname"].tolist(), df2["fname"].tolist())
}

s2 = pd.Series(degrees)
s2 = s2[s2 > N]
s2 = s2[s2.groupby(level=0).idxmax()]

df2["lname"] = df2["lname"].map(s1).fillna(df2["lname"])
df2["fname"] = df2["fname"].map(s2).fillna(df2["fname"])
df = df1.merge(df2, on=["lname", "fname"], how="outer")

The result is not what I expect. Can you help me with editing this code please? Note that I have millions of lines in df1 and millions in df2, so I need some efficiency as well.

Basically, I need to match people from df1 to people in df2. In the above example, I am matching them on last name (lname) and first name (fname). I also have a third one, which I leave out here for parsimony.

The expected result should look like:

ein ein_name    lname   fname   score
0   1001    H for Humanity  Cooper  Bradley 3
1   1500    Labor Union Cruise  Thomas  3.5
2   3000    Something something Pitt    Brad    4

Answer 1

You could try this:

from functools import cache

import pandas as pd
from fuzzywuzzy import fuzz

# First, define indices and values to check for matches
indices_and_values = [(i, value) for i, value in enumerate(df2["lname"] + df2["fname"])]

# Define helper functions to find matching rows and get corresponding score
@cache
def find_match(x):
    return [i for i, value in indices_and_values if fuzz.ratio(x, value) > 75]

def get_score(x):
    try:
        return df2.loc[x[0], "score"]
    except (KeyError, IndexError):
        return pd.NA

# Add scores to df1:
df1["score"] = (
    (df1["lname"] + df1["fname"])
    .apply(find_match)
    .apply(get_score)
)

And then:

print(df1)

    ein             ein_name   lname    fname  score
0  1001       H for Humanity  Cooper  Bradley    3.0
1  1500          Labor Union  Cruise   Thomas    3.5
2  3000  Something something    Pitt     Brad    4.0

Given the size of your dataframes, I suppose you have namesakes (identical first and last names), hence the use of @cache decorator from Python standard library in order to try speeding things up (but you can do without it).