set_index():

函数原型：DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

参数解释：

keys：列标签或列标签/数组列表，需要设置为索引的列

drop：默认为True，删除用作新索引的列

append：默认为False，是否将列附加到现有索引

inplace：默认为False，适当修改DataFrame(不要创建新对象)

verify_integrity：默认为false，检查新索引的副本。否则，请将检查推迟到必要时进行。将其设置为false将提高该方法的性能。

  1. df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7','A8', 'A9', 'A10', 'A11'],'B': ['B0', 'B1', 'B2', 'B3','B4', 'B5', 'B6', 'B7','B8', 'B9', 'B10', 'B11'],'C': ['C0', 'C1', 'C2', 'C3','C4', 'C5', 'C6', 'C7','C8', 'C9', 'C10', 'C11'],'D': ['D0', 'D1', 'D2', 'D3','D4', 'D5', 'D6', 'D7','D8', 'D9', 'D10', 'D11']})new_df_drop_t = df.set_index('A',drop=True, append=False, inplace=False, verify_integrity=False)new_df_drop_f = df.set_index('A',drop=False, append=False, inplace=False, verify_integrity=False)
  1. df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7','A8', 'A9', 'A10', 'A11'],'B': ['B0', 'B1', 'B2', 'B3','B4', 'B5', 'B6', 'B7','B8', 'B9', 'B10', 'B11'],'C': ['C0', 'C1', 'C2', 'C3','C4', 'C5', 'C6', 'C7','C8', 'C9', 'C10', 'C11'],'D': ['D0', 'D1', 'D2', 'D3','D4', 'D5', 'D6', 'D7','D8', 'D9', 'D10', 'D11']})new_df_append_t = df.set_index('A',drop=True, append=True, inplace=False, verify_integrity=False)new_df_append_f = df.set_index('A',drop=True, append=False, inplace=False, verify_integrity=False)
  1. df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7','A8', 'A9', 'A10', 'A11'],'B': ['B0', 'B1', 'B2', 'B3','B4', 'B5', 'B6', 'B7','B8', 'B9', 'B10', 'B11'],'C': ['C0', 'C1', 'C2', 'C3','C4', 'C5', 'C6', 'C7','C8', 'C9', 'C10', 'C11'],'D': ['D0', 'D1', 'D2', 'D3','D4', 'D5', 'D6', 'D7','D8', 'D9', 'D10', 'D11']})new_df_inplace_t = df.set_index('A', drop=True, append=False, inplace=True, verify_integrity=False)print (type(new_df_inplace_t))df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7','A8', 'A9', 'A10', 'A11'],'B': ['B0', 'B1', 'B2', 'B3','B4', 'B5', 'B6', 'B7','B8', 'B9', 'B10', 'B11'],'C': ['C0', 'C1', 'C2', 'C3','C4', 'C5', 'C6', 'C7','C8', 'C9', 'C10', 'C11'],'D': ['D0', 'D1', 'D2', 'D3','D4', 'D5', 'D6', 'D7','D8', 'D9', 'D10', 'D11']})new_df_inplace_f = df.set_index('A', drop=True, append=False, inplace=False, verify_integrity=False)

reset_index():

函数原型：DataFrame.resetindex(_level=None, drop=False, inplace=False, col_level=0, col_fill=’’)

参数解释:

_level：_int、str、tuple或list，默认无，仅从索引中删除给定级别。默认情况下移除所有级别。控制了具体要还原的那个等级的索引

_drop：_drop为False则索引列会被还原为普通列，否则会丢失

inplace：默认为false，适当修改DataFrame(不要创建新对象)

_col_level：_int或str，默认值为0，如果列有多个级别，则确定将标签插入到哪个级别。默认情况下，它将插入到第一级。

col_fill：对象，默认‘’，如果列有多个级别，则确定其他级别的命名方式。如果没有，则重复索引名

注：reset_index还原分为两种类型，第一种是对原DataFrame进行reset，第二种是对使用过set_index()函数的DataFrame进行reset

第一种：

  1. df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7','A8', 'A9', 'A10', 'A11'],'B': ['B0', 'B1', 'B2', 'B3','B4', 'B5', 'B6', 'B7','B8', 'B9', 'B10', 'B11'],'C': ['C0', 'C1', 'C2', 'C3','C4', 'C5', 'C6', 'C7','C8', 'C9', 'C10', 'C11'],'D': ['D0', 'D1', 'D2', 'D3','D4', 'D5', 'D6', 'D7','D8', 'D9', 'D10', 'D11']})newdf = df.set_index('A',drop=True, append=False, inplace=False, verify_integrity=False)new_reset_index = newdf.reset_index(drop=False) new_reset_index = newdf.reset_index(drop=True)

第二种：

  1. df = pd.DataFrame({ 'A': ['A0', 'A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7','A8', 'A9', 'A10', 'A11'],'B': ['B0', 'B1', 'B2', 'B3','B4', 'B5', 'B6', 'B7','B8', 'B9', 'B10', 'B11'],'C': ['C0', 'C1', 'C2', 'C3','C4', 'C5', 'C6', 'C7','C8', 'C9', 'C10', 'C11'],'D': ['D0', 'D1', 'D2', 'D3','D4', 'D5', 'D6', 'D7','D8', 'D9', 'D10', 'D11']})new_reset_index = df.reset_index(drop=False) new_reset_index = df.reset_index(drop=True)