1.DataFrame中某一列的值衍生为新的特征
#将LBL1特征的值衍生为one-hot形式的新特征 piao=df_train_log.LBL1.value_counts().index #先构造一个临时的df df_tmp=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values}) #将所有的新特征列都置为0 for i in piao: df_tmp['PIAO_'+i]=0 #进行分组便利,有这个特征就置为1,原数据每个USRID有多条记录,所以分组统计 group=df_train_log.groupby(['USRID']) for k in group.groups.keys(): t = group.get_group(k) id=t.USRID.value_counts().index[0] tmp_list=t.LBL1.value_counts().index for j in tmp_list: df_tmp['PIAO_'+j].loc[df_tmp.USRID==id]=1
2.分组统计,选出同一USRID下该变量中出现次数最多的值项
group=df_train_log.groupby(['USRID']) lt=[] list_max_lbl1=[] list_max_lbl2=[] list_max_lbl3=[] for k in group.groups.keys(): t = group.get_group(k)
#通过value_counts找出出现次数最多的项 argmx = np.argmax(t['EVT_LBL'].value_counts()) lbl1_max=np.argmax(t['LBL1'].value_counts()) lbl2_max=np.argmax(t['LBL2'].value_counts()) lbl3_max=np.argmax(t['LBL3'].value_counts()) list_max_lbl1.append(lbl1_max) list_max_lbl2.append(lbl2_max) list_max_lbl3.append(lbl3_max)
#只留下出现次数最多的项 c = t[t['EVT_LBL']==argmx].drop_duplicates('EVT_LBL')
#放入list中 lt.append(c)
#构造一个新的df df_train_log_new = pd.concat(lt)
#另外又构造了三个特征,LBL1-LBL3分别出现次数最多的项 df_train_log_new['LBL1_MAX']=list_max_lbl1 df_train_log_new['LBL2_MAX']=list_max_lbl2 df_train_log_new['LBL3_MAX']=list_max_lbl3
3.衍生出某天是否发生的ont-hot新特征
#创造临时df,星期三,星期六,星期七,都默认置为0 df_day=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values}) df_day['weekday_3']=0 df_day['weekday_6']=0 df_day['weekday_7']=0 #分组统计,有就置为1,没有置为0 group=df_train_log.groupby(['USRID']) for k in group.groups.keys(): t = group.get_group(k) id=t.USRID.value_counts().index[0] tmp_list=t.occ_dayofweek.value_counts().index for j in tmp_list: if j==3: df_day['weekday_3'].loc[df_tmp.USRID==id]=1 elif j==6: df_day['weekday_6'].loc[df_tmp.USRID==id]=1 elif j==7: df_day['weekday_7'].loc[df_tmp.USRID==id]=1