gistfile1.txt

# all of this is terrible, don't use it. 
# purpose: take a contact list from an IPHONE icloud dump, compare to a Hubspot export.
# if any names aren't similar, print out a CSV file with names you should import into Hubspot.
#
# seriously, this is horrible I wrote it very quickly there are probably better ways to do this.

import pandas as pd
from fuzzywuzzy import fuzz
import sys

threshold_for_match = 90

df_hubspot = pd.read_excel('data/hubspot-crm-exports-all-contacts.xls', na_filter=False)
df_iphone = pd.read_excel('data/iphone export.xlsx', na_filter=False)

contacts = {}

print('\npreping iphone data')
for index, row in df_iphone.iterrows():
    fullname = row['First Name'] + ' ' + row['Last Name']
    contacts[fullname.strip()] = {'df': row, 'include': True}
    print('.', end='')

print('\ncross-checking iphone data')
c = 0
for index, row in df_hubspot.iterrows():
    fullname = row['First Name'] + ' ' + row['Last Name']
    fullname = fullname.strip()

    c += 1
    if c % 10 == 0:
        print('.', end='')
        sys.stdout.flush()

    for name, contact in contacts.items():
        if not name or not fullname:
            continue

        score = fuzz.ratio(name, fullname)
        if score > threshold_for_match:
            contact['include'] = False
            # if name != fullname:
            #    print("NAMES ARE CLOSE ENOUGH TO EXCLUDE: " + name + ' ' + fullname)
            break

print('\n')

dfs = []
for name, contact in contacts.items():
    if contact['include']:
        dfs.append(contact['df'].to_dict())

import csv

keys = dfs[0].keys()
with open('c:/tmp/output.csv', 'w', encoding='utf-8-sig', newline='') as output_file:
    dict_writer = csv.DictWriter(output_file, keys)
    dict_writer.writeheader()
    dict_writer.writerows(dfs)