左侧的CSV文件有五列，其中.application列包含了几种应用程序类型，并用;分隔。根据app、device和district类型，我希望预测target。但首先，我想将文件转换为右侧的数据框架，以便应用机器学习。

我该如何使用Python来实现这一点？

回答：

你需要对application列应用多热编码，对其他列应用单热编码。

这是我的解决方案！

>>> import pandas as pd>>> import numpy as np>>> df = pd.DataFrame({'number': np.random.randint(0,10,size=5),                  'device': np.random.choice(['a','b'],size=5),                  'application': ['app2;app3','app1','app2;app4', 'app1;app2', 'app1'],                  'district': np.random.choice(['aa', 'bb', 'cc'],size=5)})>>> df    application device  district    number0   app2;app3   b         aa    31   app1        a         cc    72   app2;app4   a         aa    33   app1;app2   b         bb    94   app1        a         cc    4from sklearn.preprocessing import OneHotEncoder, MultiLabelBinarizermlb = MultiLabelBinarizer()# Assuming appl names are separated by ;mhv = mlb.fit_transform(df['application'].apply(lambda x: set(x.split(';'))))df_out = pd.DataFrame(mhv,columns=mlb.classes_)enc = OneHotEncoder(sparse=False)ohe_vars = ['device','district'] # specify the list of columns hereohv = enc.fit_transform(df.loc[:,ohe_vars])ohe_col_names = ['%s_%s'%(var,cat) for var,cats in zip(ohe_vars, enc.categories_) for cat in cats]df_out.assign(**dict(zip(ohe_col_names,ohv.T)))df_out