我想创建一个分类变量type，其中样本标记为"；肿瘤"；如果指数的最后一个分量在01和09之间(即01-09＝肿瘤(。否则，样品为"；正常的"；(即10-19=正常(。
clin_kipan = clin_kipan.assign(type=(
clin_kipan.index
.str.split('-')
.str[-1]
.str.contains(r'^0[1-9]', regex=True)
)).replace({'type':{True:'tumor', False:'normal'}}).dropna(axis=1)
数据(作为字典(注意：我删除了部分数据以适应stackoverflow。
meth_450_10k_kipan.iloc[：，0].to_dict((
{'TCGA-2K-A9WE-01A': 0.461440642939772,
'TCGA-2Z-A9J1-01A': 0.595894468074615,
'TCGA-2Z-A9J2-01A': 0.481304782143526,
'TCGA-2Z-A9J3-01A': 0.553849599144766,
'TCGA-2Z-A9J5-01A': 0.184349035247422,
'TCGA-2Z-A9J6-01A': 0.368324568853624,
'TCGA-2Z-A9J7-01A': 0.17338171975844,
'TCGA-2Z-A9J8-01A': 0.805217631945182,
'TCGA-2Z-A9J9-01A': 0.113336454896178,
'TCGA-2Z-A9JD-01A': 0.451013557269167,
'TCGA-2Z-A9JE-01A': 0.0734337852294042,
'TCGA-2Z-A9JG-01A': 0.452956990020555,
'TCGA-2Z-A9JI-01A': 0.606187976542221,
'TCGA-2Z-A9JJ-01A': 0.0960629248660591,
'TCGA-2Z-A9JK-01A': 0.588377732872447,
'TCGA-2Z-A9JL-01A': 0.379404812465867,
'TCGA-2Z-A9JM-01A': 0.33009261875889,
'TCGA-2Z-A9JN-01A': 0.123562375369795,
'TCGA-2Z-A9JO-01A': 0.486619960274097,
'TCGA-2Z-A9JP-01A': 0.612453055148181,
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'TCGA-UZ-A9PZ-01A': 0.200317023824517,
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'TCGA-V9-A7HT-01A': 0.400421235791175,
'TCGA-WN-A9G9-01A': 0.713709756524377,
'TCGA-WN-AB4C-01A': 0.447091219579516,
'TCGA-Y8-A894-01A': 0.742301356187307,
'TCGA-Y8-A895-01A': 0.10049731725205,
'TCGA-Y8-A896-01A': 0.276383911326578,
'TCGA-Y8-A897-01A': 0.374250450339407,
'TCGA-Y8-A898-01A': 0.310842285574522,
'TCGA-Y8-A8RY-01A': 0.552137271695761,
'TCGA-Y8-A8RZ-01A': 0.497271965133314,
'TCGA-Y8-A8S0-01A': 0.264547845506278,
'TCGA-Y8-A8S1-01A': 0.581694254362101}
预期结果：
行的总数为867，其中662个预期为tumor，其余为normal。
len(meth_450_10k_kipan)
867
meth_450_10k_kipan.index.str.split('-').str[-1].str.contains(r'^0[1-9]', regex=True).sum()
662
您可以分两步完成，提取标签，然后根据其值进行过滤，以分配所需的输出：
df = pd.Series(d).to_frame() # assuming d to be the dict with the data
labels = df.index.str.extract(r"-(d+)D+$")[0].astype(int)
df["labels"] = pd.Categorical((labels < 10).map({True: "tumor", False: "normal"}))
>> df["labels"].unique()
['tumor', 'normal']
Categories (2, object): ['normal', 'tumor']
>> df["labels"]
TCGA-2K-A9WE-01A tumor
TCGA-2Z-A9J1-01A tumor
TCGA-2Z-A9J2-01A tumor
TCGA-2Z-A9J3-01A tumor
TCGA-2Z-A9J5-01A tumor
...
TCGA-Y8-A898-01A tumor
TCGA-Y8-A8RY-01A tumor
TCGA-Y8-A8RZ-01A tumor
TCGA-Y8-A8S0-01A tumor
TCGA-Y8-A8S1-01A tumor
Name: labels, Length: 637, dtype: category
Categories (2, object): ['normal', 'tumor']