如何根据某些值的相似度更新dataframe列？

import pandas as pd
import numpy as np

data = {'A': [1, 2, 3, 4, 5],
        'B': [0.1, 0.2, 0.3, 0.4, 0.5],
        'C': ['apple', 'banana', 'orange', 'grape', 'kiwi']}
df = pd.DataFrame(data)

def cosine_similarity(a, b):
    dot_product = np.dot(a, b)
    norm_a = np.linalg.norm(a)
    norm_b = np.linalg.norm(b)
    similarity = dot_product / (norm_a * norm_b)
    return similarity

def update_column(row, column_name, threshold):
    values = df[column_name].values
    new_value = row[column_name]
    for i, value in enumerate(values):
        similarity = cosine_similarity(new_value, value)
        if similarity >= threshold:
            df.at[i, column_name] = new_value

threshold = 0.8
df.apply(lambda row: update_column(row, 'A', threshold), axis=1)