如何将数据集中多个场地映射为对应国家并生成新列？

步骤1：创建场地-国家映射字典 #

先把所有给定场地对应的国家整理成字典，确保覆盖所有条目：

ground_to_country = {
    'Auckland': 'New Zealand',
    'Southampton': 'England',
    'Johannesburg': 'South Africa',
    'Brisbane': 'Australia',
    'Bristol': 'England',
    'Khulna': 'Bangladesh',
    'Wellington': 'New Zealand',
    'Sydney': 'Australia',
    'The Oval': 'England',
    'Nairobi (Gym)': 'Kenya',
    'Durban': 'South Africa',
    'Cape Town': 'South Africa',
    'Brabourne': 'India',
    'Perth': 'Australia',
    'Gqeberha': 'South Africa',
    'Melbourne': 'Australia',
    'Christchurch': 'New Zealand',
    'Karachi': 'Pakistan',
    'Manchester': 'England',
    'Bridgetown': 'Barbados',
    'Belfast': 'England',
    'King City (NW)': 'Canada',
    'Hamilton': 'New Zealand',
    'Colombo (RPS)': 'Sri Lanka',
    'Port of Spain': 'Trinidad and Tobago',
    'Centurion': 'South Africa',
    'Dubai (DSC)': 'United Arab Emirates',
    "Lord's": 'England',
    'Nottingham': 'England',
    'Basseterre': 'Saint Kitts and Nevis',
    'Nagpur': 'India',
    'Mohali': 'India',
    'Colombo (PSS)': 'Sri Lanka',
    'Abu Dhabi': 'United Arab Emirates',
    'Hobart': 'Australia',
    'Providence': 'Guyana',
    'Gros Islet': 'Saint Lucia',
    'North Sound': 'Antigua and Barbuda',
    'Lauderhill': 'United States',
    'Harare': 'Zimbabwe',
    'Birmingham': 'England',
    'Cardiff': 'England',
    'Bloemfontein': 'South Africa',
    'Kimberley': 'South Africa',
    'Adelaide': 'Australia',
    'Pallekele': 'Sri Lanka',
    'Mirpur': 'Bangladesh',
    'Eden Gardens': 'India',
    'Mombasa': 'Kenya',
    'ICCA Dubai': 'United Arab Emirates',
    'Hambantota': 'Sri Lanka',
    'The Hague': 'Netherlands',
    'Chester-le-Street': 'England',
    'Chennai': 'India',
    'Pune': 'India',
    'Wankhede': 'India',
    'East London': 'South Africa',
    'Bengaluru': 'India',
    'Ahmedabad': 'India',
    'Sharjah': 'United Arab Emirates',
    'Windhoek': 'Namibia',
    'Bulawayo': 'Zimbabwe',
    'Aberdeen': 'England',
    'Kingstown': 'Saint Vincent and the Grenadines',
    'Rajkot': 'India',
    'Chattogram': 'Bangladesh',
    'Kingston': 'Jamaica',
    'Sylhet': 'Bangladesh',
    'Roseau': 'Dominica',
    'Lahore': 'Pakistan',
    'Bready': 'England',
    'Edinburgh': 'England',
    'Dublin (Malahide)': 'Ireland',
    'Dharamsala': 'India',
    'Cuttack': 'India',
    'Mount Maunganui': 'New Zealand',
    'Ranchi': 'India',
    'Visakhapatnam': 'India',
    'Delhi': 'India',
    'Napier': 'New Zealand',
    'Kanpur': 'India',
    'Geelong': 'Australia',
    'Greater Noida': 'India',
    'Taunton': 'England',
    'Guwahati': 'India',
    'Potchefstroom': 'South Africa',
    'Thiruvananthapuram': 'India',
    'Indore': 'India',
    'Nelson': 'New Zealand',
    'Dehradun': 'India',
    'Rotterdam': 'Netherlands',
    'Deventer': 'Netherlands',
    'Amstelveen': 'Netherlands',
    'Lucknow': 'India',
    'Carrara': 'Australia',
    'Al Amerat': 'Oman',
    'ICCA 2 Dubai': 'United Arab Emirates',
    'Canberra': 'Australia',
    'Hyderabad': 'India',
    "St George's": 'Grenada',
    'Rawalpindi': 'Pakistan',
    'Paarl': 'South Africa',
    'Dunedin': 'New Zealand',
    'Coolidge': 'Anguilla',
    'Leeds': 'England',
    'Dublin': 'Ireland',
    'Jaipur': 'India',
    'Tarouba': 'Trinidad and Tobago'
}

步骤2：用Pandas矢量化映射生成新列 #

对于大型数据集，矢量化操作是最高效的方式，避免低效的逐行循环。直接使用pandas.Series.map()方法：

import pandas as pd

# 假设你的数据集为df，Ground是目标列
df['Country'] = df['Ground'].map(ground_to_country)

补充说明 #

• 该方法时间复杂度为O(n)，常数项远小于逐行循环，处理百万级数据也能快速完成。
• 若担心存在未匹配的场地，可添加fillna()处理缺失值：

  df['Country'] = df['Ground'].map(ground_to_country).fillna('Unknown')
  

内容的提问来源于stack exchange，提问作者UDAY KORAM