（1）读取“GSE83148_series_matrix.txt”基因芯片文件和平台“GPL570-55999.txt”(跳过注释行)，分别命名为df1和df2 ​ ​ （2）清除df1与df2中的重复数据和缺失数据(df1清除存在NaN的行，df2清除“Gene Symbol”列存在NaN的行) ​ （3）df2新增一列“gene”其值为“Gene Symbol”列的第一个基因名，df1的“ID_REF”列重命名为“ID”列，df2保留“ID”和“gene”列然后与df1合并为df，df中gene名相同的记录表达量取平均值，打印df ​ （4）转置df，并在后面添加一列label，以“N”标识正常样本，以“C”标识肿瘤样本，这些信息可以从“GSE83148_series_matrix.txt”基因芯片文件的“!Sample_title”行提取，打印并保存df ​ （5）将“label”列转换为哑变量(k-1)，并作为df的最后一列，删除label列 ​ （6）任选两个基因，分别对其表达量进行离差标准化和标准差标准化（结果不需要保存到df中） ​ （7）尝试任选一个基因，离散化其表达量（不需要保存） ​ （8）统计df的每个基因在肿瘤和正常样本上表达量的平均值，并保存为A ​ （9）提前A中表达量差值的绝对值最大的10个基因名¶

很好，以下是你的答案： (1) ```python import pandas as pd df1 = pd.read_csv('GSE83148_series_matrix.txt', sep='\t', skiprows=79) df2 = pd.read_csv('GPL570-55999.txt', sep='\t', skiprows=31) ``` (2) ```python df1.dropna(inplace=True) df2.dropna(subset=['Gene Symbol'], inplace=True) df2['gene'] = df2['Gene Symbol'].str.split(' /// ').str[0] df1.rename(columns={'ID_REF': 'ID'}, inplace=True) df = pd.merge(df1, df2[['ID', 'gene']], on='ID') df = df.groupby('gene').mean().reset_index() ``` (3) ```python df = df.T.reset_index() df.columns = df.iloc[0] df = df[1:].reset_index(drop=True) df['label'] = df['!Sample_title'].str.extract(r'(N|C)') df = df.iloc[:, :-1] dummy_label = pd.get_dummies(df['label'], prefix='label', drop_first=True) df = pd.concat([df.iloc[:, :-1], dummy_label], axis=1) df.drop('!Sample_title', axis=1, inplace=True) ``` (4) ```python df = df.T.reset_index() df.columns = df.iloc[0] df = df[1:].reset_index(drop=True) df['label'] = df['!Sample_title'].str.extract(r'(N|C)') df = df.iloc[:, :-1] dummy_label = pd.get_dummies(df['label'], prefix='label', drop_first=True) df = pd.concat([df.iloc[:, :-1], dummy_label], axis=1) df.drop('!Sample_title', axis=1, inplace=True) ``` (5) ```python from sklearn.preprocessing import LabelEncoder, OneHotEncoder label_encoder = LabelEncoder() df['label'] = label_encoder.fit_transform(df['label']) one_hot_encoder = OneHotEncoder(categories='auto', sparse=False) dummy_label = one_hot_encoder.fit_transform(df[['label']]) dummy_label = pd.DataFrame(dummy_label, columns=['label_N', 'label_C']) df = pd.concat([df.iloc[:, :-1], dummy_label], axis=1) df.drop('label', axis=1, inplace=True) ``` (6) ```python from sklearn.preprocessing import StandardScaler # 离差标准化 scaler1 = StandardScaler() df['gene1_normalized'] = scaler1.fit_transform(df[['gene1']]) # 标准差标准化 scaler2 = StandardScaler(with_std=False) df['gene2_normalized'] = scaler2.fit_transform(df[['gene2']]) ``` (7) ```python # 离散化 df['gene3_discretized'] = pd.cut(df['gene3'], bins=3, labels=['low', 'medium', 'high']) ``` (8) ```python A = df.groupby('gene').mean()[['label_N', 'label_C']] ``` (9) ```python A['diff'] = abs(A['label_N'] - A['label_C']) top10_genes = A.nlargest(10, 'diff').index.tolist() ```