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>>> dataset.columns
Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',
'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',
'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',
'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],
dtype='object')

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>>> new_columns
Index(['age_0', 'job_1', 'marital_2', 'education_3', 'default_4', 'housing_5',
'loan_6', 'contact_7', 'month_8', 'day_of_week_9', 'duration_10',
'campaign_11', 'pdays_12', 'previous_13', 'poutcome_14',
'emp.var.rate_15', 'cons.price.idx_16', 'cons.conf.idx_17',
'euribor3m_18', 'nr.employed_19', 'y_20'],
dtype='object')

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>>> # 先解决`new_columns`的推导问题
>>> # 列表推导
>>> new_columns_list = [column_str+'_'+str(i) for i ,column_str in enumerate(dataset.columns)]
>>> # 类型转换
>>> new_columns = pd.core.indexes.base.Index(new_columns_list)
>>> dataset.columns = new_columns

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>>> # 注:mapper 多运用 lambda 表达式
>>> # 但我似乎没有找到在 lambda 表达式中改变两个值的方法
>>> # 所以只能蹩脚地用一个全局变量i, 和映射函数mapper()
>>> # 希望大家能帮我找到方法
>>> i = 0
>>> def mapper(x): # 映射函数即 mapper
global i
x += '_' + str(i)
i += 1
return x
>>> dataset.columns.map(mapper)

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>>> # 此处先用字典推导法
>>> new_dict = {
key:key+'_'+str(i)
for i, key in enumerate(dataset.columns)
}
>>> dataset.rename(columns=new_dict, inplace=True)

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>>> # 原博文依然用到了 lambda 表达式
>>> # 我就再生搬硬套一次, 把上面的复制过来
>>> # 蹩脚地用一个全局变量i, 和映射函数mapper()
>>> i = 0
>>> def mapper(x): # 映射函数即 mapper
global i
x += '_' + str(i)
i += 1
return x
dataset.rename(columns=mapper, inplace=True)