自学教程：浅谈dataframe两列相乘构造新特征

import pandas as pdlists=pd.DataFrame({'a':[1,2,3,4,5,6,7,8,9]})lists['b']=(lists['a']<6).mul(lists['a']>4)

df["new"]=df3["rate"]*df3["duration"]

#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dfdf_tmp=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values})#将所有的新特征列都置为0for 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

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dfdf_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_lbl1df_train_log_new['LBL2_MAX']=list_max_lbl2df_train_log_new['LBL3_MAX']=list_max_lbl3

#创造临时df，星期三，星期六，星期七，都默认置为0df_day=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values})df_day['weekday_3']=0df_day['weekday_6']=0df_day['weekday_7']=0#分组统计，有就置为1，没有置为0group=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

#首先将日期转化为时间戳，并赋予一个新特征tmp_list=[]for i in df_train_log.OCC_TIM:    d=datetime.datetime.strptime(str(i),"%Y-%m-%d %H:%M:%S")    evt_time = time.mktime(d.timetuple())    tmp_list.append(evt_time)df_train_log['time']=tmp_list#每下一行减去上一行，得到app停留时间df_train_log['diff_time']=df_train_log.time-df_train_log.time.shift(1)#构造一个新的dataFrame，分组得到查看app的天数df_time=pd.DataFrame({'USRID':df_train_log.drop_duplicates('USRID').USRID.values})#有几天查看df_time['days']=0group=df_train_log.groupby(['USRID'])for k in group.groups.keys():    t = group.get_group(k)    id=set(t.USRID).pop()    df_time['days'].loc[df_time.USRID==id]= len(t.occ_day.value_counts().index)#去掉一些异常时间戳，比如间隔两天的相减，肯定不合适，na的也去掉了df_train_log=df_train_log[(df_train_log.diff_time>0)&(df_train_log.diff_time<8000)]#累计停留时间group_stayTime=df_train_log['diff_time'].groupby(df_train_log['USRID']).sum()#创造新的dfdf_tmp=pd.DataFrame({'USRID':list(group_stayTime.index.values),'stay_time':list(group_stayTime.values)})#合并成一个新的dfdf=pd.merge(df_time,df_tmp,on=['USRID'],how='left')#合并后，缺失的停留时间，置为0df.fillna(0,axis=1,inplace=True)