使用正则表达式分组批量匹配多模式重命名Pandas DataFrame列

替换规则 #

• male → m_
• female → f_
• working → w
• population → pop
• in the age group 0 to 6 years → _minor

方法一：使用pandas字符串方法链式调用 #

直接通过str.replace链式调用处理每个规则，按顺序执行可避免模式间干扰：

import pandas as pd

# 初始化示例DataFrame
cols_2 = ['state', 'population', 'male population', 'female population', 'working population', 'male  working population', 'female working population', 'female population in the age group 0 to 6 years', 'male population in the age group 0 to 6 years', 'population in the age group 0 to 6 years']
df = pd.DataFrame(columns=cols_2)

# 批量替换列名
df.columns = df.columns.str.replace('male ', 'm_')\
                        .str.replace('female ', 'f_')\
                        .str.replace('working ', 'w')\
                        .str.replace(' population', 'pop')\
                        .str.replace('in the age group 0 to 6 years', '_minor')

# 查看结果
print(df.columns.tolist())

方法二：正则表达式+字典映射 #

如果需要更灵活的模式匹配逻辑，可结合re.sub与替换字典，遍历处理每个列名：

import pandas as pd
import re

cols_2 = ['state', 'population', 'male population', 'female population', 'working population', 'male  working population', 'female working population', 'female population in the age group 0 to 6 years', 'male population in the age group 0 to 6 years', 'population in the age group 0 to 6 years']
df = pd.DataFrame(columns=cols_2)

# 定义替换规则字典
replace_rules = {
    r'male ': 'm_',
    r'female ': 'f_',
    r'working ': 'w',
    r' population': 'pop',
    r'in the age group 0 to 6 years': '_minor'
}

# 定义单列名处理函数
def process_col_name(col):
    for pattern, replacement in replace_rules.items():
        col = re.sub(pattern, replacement, col)
    return col

# 应用到所有列名
df.columns = [process_col_name(col) for col in df.columns]

# 查看结果
print(df.columns.tolist())

最终替换后的列名列表 #

['state', 'pop', 'm_pop', 'f_pop', 'wpop', 'm_wpop', 'f_wpop', 'f_pop_minor', 'm_pop_minor', 'pop_minor']

内容的提问来源于stack exchange，提问作者Adit Agrawal