import pandas as pd
import numpy as np
import uuid

# generate dummy data
colnames = ["keyword_id", "url_hash"]
keywords = [str(uuid.uuid4()) for _ in range(100)]
url_hash = [str(uuid.uuid4()) for _ in range(35)]

# keyword_id, url_hash
df = pd.DataFrame({
    "keyword_id": np.random.choice(keywords, 1000),
    "url_hash": np.random.choice(url_hash, 1000)
})


table = {}
def find(val):
    while val != table[val]:
        table[val] = table[table[val]]
        val = table[val]
    return table[val]

def union(vals):
    for val in vals:
        table[val] = min(vals)


for url, key_ids in df.groupby("url_hash")["keyword_id"]:
    table[url] = url
    for k_id in key_ids:
        table[k_id] = url

for key_id, urls in df.groupby("keyword_id")["url_hash"]:
    union(urls.values)


df["group"] = df["keyword_id"].apply(find)