>>> 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')

>>> 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')

>>> # 先解決`new_columns`的推導(dǎo)問(wèn)題
>>> # 列表推導(dǎo)
>>> new_columns_list = [column_str+'_'+str(i) for i ,column_str in enumerate(dataset.columns)]
>>> # 類型轉(zhuǎn)換
>>> new_columns = pd.core.indexes.base.Index(new_columns_list)
>>> dataset.columns = new_columns

>>> # 注:mapper 多運(yùn)用 lambda 表達(dá)式
>>> # 但我似乎沒(méi)有找到在 lambda 表達(dá)式中改變兩個(gè)值的方法
>>> # 所以只能蹩腳地用一個(gè)全局變量i, 和映射函數(shù)mapper()
>>> # 希望大家能幫我找到方法

>>> i = 0
>>> def mapper(x): # 映射函數(shù)即 mapper
  global i
  x += '_' + str(i)
  i += 1
  return x
>>> dataset.columns.map(mapper)

>>> # 此處先用字典推導(dǎo)法
>>> new_dict = {
  key:key+'_'+str(i)
  for i, key in enumerate(dataset.columns)
  }
>>> dataset.rename(columns=new_dict, inplace=True)

>>> # 原博文依然用到了 lambda 表達(dá)式
>>> # 我就再生搬硬套一次, 把上面的復(fù)制過(guò)來(lái)
>>> # 蹩腳地用一個(gè)全局變量i, 和映射函數(shù)mapper()

>>> i = 0
>>> def mapper(x): # 映射函數(shù)即 mapper
  global i
  x += '_' + str(i)
  i += 1
  return x
dataset.rename(columns=mapper, inplace=True)