重命名重复的索引值pandas DataFrame

Question

我有一个包含一些重复索引值的DataFrame:

df1 =  pd.DataFrame( np.random.randn(6,6), columns = pd.date_range('1/1/2010', periods=6), index = {"A", "B", "C", "D", "E", "F"})
df1.rename(index = {"C": "A", "B": "E"}, inplace = 1)

ipdb> df1
      2010-01-01  2010-01-02  2010-01-03  2010-01-04  2010-01-05  2010-01-06
 A   -1.163883    0.593760    2.323342   -0.928527    0.058336   -0.209101
 A   -0.593566   -0.894161   -0.789849    1.452725    0.821477   -0.738937
 E   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
 F    1.707686    0.323213    0.048503    1.168898    0.002662   -1.988825
 D    0.403028   -0.879873   -1.809991   -1.817214   -0.012758    0.283450
 E   -0.224405   -1.803301    0.582946    0.338941    0.798908    0.714560

我想只更改重复值的名称,并获取如下所示的DataFrame:

ipdb> df1
     2010-01-01  2010-01-02  2010-01-03  2010-01-04  2010-01-05  2010-01-06
A   -1.163883    0.593760    2.323342   -0.928527    0.058336   -0.209101
A_dp   -0.593566   -0.894161   -0.789849    1.452725    0.821477   -0.738937
E   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
F    1.707686    0.323213    0.048503    1.168898    0.002662   -1.988825
D    0.403028   -0.879873   -1.809991   -1.817214   -0.012758    0.283450
E_dp   -0.224405   -1.803301    0.582946    0.338941    0.798908    0.714560

我的方法:

(i)使用新名称创建字典

old_names = df1[df1.index.duplicated()].index.values
new_names = df1[df1.index.duplicated()].index.values + "_dp"
dictionary = dict(zip(old_names, new_names))

(ii)仅重命名重复的值

df1.loc[df1.index.duplicated(),:].rename(index = dictionary, inplace = True)

然而,这似乎不起作用.

Answer 1

你可以使用Index.where:

df1.index = df1.index.where(~df1.index.duplicated(), df1.index + '_dp')
print (df1)
      2010-01-01  2010-01-02  2010-01-03  2010-01-04  2010-01-05  2010-01-06
A      -1.163883    0.593760    2.323342   -0.928527    0.058336   -0.209101
A_dp   -0.593566   -0.894161   -0.789849    1.452725    0.821477   -0.738937
E      -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
F       1.707686    0.323213    0.048503    1.168898    0.002662   -1.988825
D       0.403028   -0.879873   -1.809991   -1.817214   -0.012758    0.283450
E_dp   -0.224405   -1.803301    0.582946    0.338941    0.798908    0.714560

如果需要删除重复索引到唯一:

print (df1)
   2010-01-01  2010-01-02  2010-01-03  2010-01-04  2010-01-05  2010-01-06
A   -1.163883    0.593760    2.323342   -0.928527    0.058336   -0.209101
A   -0.593566   -0.894161   -0.789849    1.452725    0.821477   -0.738937
E   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
E   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
E   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
F    1.707686    0.323213    0.048503    1.168898    0.002662   -1.988825
D    0.403028   -0.879873   -1.809991   -1.817214   -0.012758    0.283450
E   -0.224405   -1.803301    0.582946    0.338941    0.798908    0.714560

df1.index = df1.index + df1.groupby(level=0).cumcount().astype(str).replace('0','')
print (df1)
    2010-01-01  2010-01-02  2010-01-03  2010-01-04  2010-01-05  2010-01-06
A    -1.163883    0.593760    2.323342   -0.928527    0.058336   -0.209101
A1   -0.593566   -0.894161   -0.789849    1.452725    0.821477   -0.738937
E    -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
E1   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
E2   -0.670305   -1.788403    0.134790   -0.270894    0.672948    1.149089
F     1.707686    0.323213    0.048503    1.168898    0.002662   -1.988825
D     0.403028   -0.879873   -1.809991   -1.817214   -0.012758    0.283450
E3   -0.224405   -1.803301    0.582946    0.338941    0.798908    0.714560