Matching a list of names to files when the names are reversed

I have a .csv with a column called 'Name' listing individuals in Firstname MI. Lastname format:

• Stephen A. Johnson.

And I have a folder full of files made by these individuals in lastnamefirstname format:

• johnsonstephen_78938

Using the list of names I need to find the files that match and save those files to a separate folder.

What is the best way to do this?

Model

Let's prepare some space to model:

# necessary part
import pandas as pd
from pathlib import Path
from shutil import copy     # consider copy2 as alternative if metadata should be copied as well

# for demonstration purpose
from tempfile import TemporaryDirectory 
from os import listdir
from io import StringIO

data = '''
Stephen A. Johnson
Stephen B. Johnson
Albert J. Reiß
'''
csv_source = StringIO(data)

source = TemporaryDirectory(); src = Path(source.name)
destin = TemporaryDirectory(); dst = Path(destin.name)

filenames = [
    'johnsonstephen_30',
    'johnsonstephen_40',
    'reissalbert_50',     # should be copied with `casefold` and ignored with `lower`
    'gibberish_10',       # should be ignored
]

for file in filenames:
    (src/file).touch()

Now the main part. There's at least one pitfall: casefold versus lower when applied to the original names. This may influence on the file names selected and should be consciously resolved on your side. I use casefold and the name "Reiß" to demonstrate how it might appear.

# read the sequence of names
person = pd.read_csv(csv_source, header=None).squeeze()

# preparing name stems
ersatz = (
    person
    .str.casefold()     # not identical to `lower`, select one according to naming policy
    .str.split()
    .str[::-2]          # it won't work if the middle name doesn't exist.
    .str.join('')
    .unique()
)

# copying files of interest
for name in ersatz:
    for file in src.rglob(f'{name}_*'):
        copy(file, dst)     

Check the result and clean up the space:

print(f'Source:      {listdir(src)}')
print(f'Destination: {listdir(dst)}')

source.cleanup()
destin.cleanup()

Expected output:

Source:      ['gibberish_1234', 'johnsonstephen_40', 'johnsonstephen_30', 'reissalbert_50']
Destination: ['johnsonstephen_40', 'johnsonstephen_30', 'reissalbert_50']

P.S. Alternatively, when loading a csv file, we can interpret whitespace characters as a delimiter:

# read the sequence of names
person = pd.read_csv(
    csv_source, 
    header=None, 
    delim_whitespace=True, 
    usecols=[0,2],
    names=['firstname','lastname'],
)
ersatz = person['lastname'].str.casefold() + person['firstname'].str.casefold()

Real life

from pandas import read_csv
from pathlib import Path
from shutil import copy     

# declare csv-file and source/destination folders
source = 'path/to/folder/with/files'
destination = 'folder/to/copy/files/into'
csv_source = 'path/to/comma_separated_data_with_column_Name.csv'

# main script
src = Path(source)
dst = Path(destination)

person = read_csv(csv_source)['Name'].squeeze()
ersatz = (
    person
    .str.casefold()
    .str.split()
    .pipe(lambda seq: seq.str[-1] + seq.str[0])    # rely only on first and last name
    .unique()
)

for name in ersatz:
    for file in src.rglob(f'{name}_*'):
        copy(file, dst)