如何检测CSV数据集中的姓名（名与姓）是否为英语（英式/美式）起源？

Hey there! Let's break down your problem and walk through each of your proposed solutions, plus add some practical tips to help you tackle that 9k-row CSV effectively. First off, great job brainstorming three solid angles—each has its own strengths, and combining them can give you more reliable results, especially given the diverse population in your survey area.

1. N-gram (Triplet/Quadruplet) Analysis #

This approach leverages the unique character patterns of English names, and it's a smart way to do a quick initial filter. Here's how to make it work:

• Preprocess first: Standardize all names to lowercase, strip out any non-alphabetical special characters (be cautious with hyphens/apostrophes though—some English names do use these!), to ensure consistent pattern matching.
• Build your n-gram reference: Grab the English name datasets you mentioned (like the early 20th-century UK names) and generate a frequency count of all 3-letter or 4-letter sequences. You can use Python's collections.Counter for this easily.
• Set a threshold: For each name in your CSV, calculate what percentage of its n-grams appear in your English reference set. If it’s above a threshold you define (say 70%), flag it as likely English.
• Caveat: Non-English names might share some common n-grams, so this is best for narrowing down candidates, not as a final judgment.

Quick code snippet to get you started:

from collections import Counter
import re

def generate_ngrams(text, n=3):
    # Clean text and generate n-grams
    cleaned_text = re.sub(r'[^a-zA-Z]', '', text.lower())
    return [cleaned_text[i:i+n] for i in range(len(cleaned_text)-n+1)]

# Load your English name dataset into a list called english_names
english_ngrams = Counter()
for name in english_names:
    english_ngrams.update(generate_ngrams(name))

def is_likely_english_ngram(name, threshold=0.7):
    name_ngrams = generate_ngrams(name)
    if not name_ngrams:
        return False
    matching = sum(1 for ngram in name_ngrams if ngram in english_ngrams)
    return (matching / len(name_ngrams)) >= threshold

2. Matching Against Established English Name Datasets #

This method is more accurate than n-grams because you’re comparing against known English names. The key here is to use fuzzy matching instead of exact matches, since name spellings often vary (think "Catherine" vs "Kathryn").

• Pick diverse datasets: Combine your early 20th-century UK names with modern American/British name lists to avoid missing newer or region-specific names.
• Use fuzzy matching tools: Python's fuzzywuzzy library is perfect for this—you can set a score threshold (like 80 out of 100) to count close matches as English.

Example code:

from fuzzywuzzy import process

# Load your English first and last name lists into separate variables
english_first_names = [...]
english_last_names = [...]

def is_likely_english_name(target_name, reference_list, threshold=80):
    # Match lowercase to avoid case sensitivity
    best_match, score = process.extractOne(target_name.lower(), [name.lower() for name in reference_list])
    return score >= threshold

3. Using Python Libraries/Pre-trained Models #

This is the most efficient option for large datasets, as there are tools built specifically for name origin prediction.

• Try ethnicolr: This library is designed to predict the ethnic origin of names, and it supports English as a category. It uses pre-trained models based on public datasets, so you don’t have to build everything from scratch.
• Workflow example:

  import pandas as pd
  from ethnicolr import pred_wiki_name
  
  # Load your CSV
  df = pd.read_csv("your_survey_data.csv")
  # Predict first name origin
  df_with_first_pred = pred_wiki_name(df, "first_name")
  # Predict last name origin
  df_with_last_pred = pred_wiki_name(df, "last_name")
  # Add binary flags for English origin
  df["is_english_first"] = df_with_first_pred["pred"] == "English"
  df["is_english_last"] = df_with_last_pred["pred"] == "English"
  

• Note: Like all statistical models, this isn’t 100% perfect, but it’s great for processing thousands of rows quickly.

Bonus Tips to Improve Accuracy #

• Combine methods: Use n-grams to filter out obvious non-English names first, then run the remaining through dataset matching or ethnicolr—this saves time and reduces false positives.
• Validate manually: Randomly sample 5-10% of your results to check how well your methods are working. Adjust thresholds or add edge cases (like hyphenated names) based on what you find.
• Embrace the imperfection: As you noted, binary "English/non-English" classification has flaws. You might want to add a "uncertain" category for names that fall right on the threshold, instead of forcing a hard yes/no.

内容的提问来源于stack exchange，提问作者Marcel Campion