我有一個看起來像這樣的數據框：df = pd.DataFrame({'VisitorID': [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000], 'EpochTime': [1554888560, 1554888560, 1554888560, 1554888560, 1554888560, 1521333510, 1521333510, 1521333510], 'HitTime': [1400, 5340, 7034, 11034, 13059, 990, 4149, 6450], 'HitNumber':[23, 54, 55, 65, 110, 14, 29, 54], 'PagePath':['orders/details', 'orders/payment', 'orders/afterpayment', 'orders/myorders', 'customercare', 'orders/details', 'orders/payment', 'orders/myorders']})print(df) VisitorID EpochTime HitTime HitNumber PagePath0 1000 1554888560 1400 23 orders/details1 1000 1554888560 5340 54 orders/payment2 1000 1554888560 7034 55 orders/afterpayment3 1000 1554888560 11034 65 orders/myorders4 1000 1554888560 13059 110 customercare5 1000 1521333510 990 14 orders/details6 1000 1521333510 4149 29 orders/payment7 1000 1521333510 6450 54 orders/myorders實際上，我的數據框是 +- 1000 萬行。并且有兩倍的列。數據由顯示客戶行為的網站數據組成。我想要做什么為了分析客戶在到達被跟蹤的第一頁之前在網站上的時間，我想在每個組上方添加一行，從列中復制頂行的值：訪客編號大紀元但為列提供新值：命中時間 = 0命中數 = 0頁面路徑 = Home信息：VisitorID+的組合EpochTime使一個組獨一無二。我使用以下代碼實現了這一點，但運行需要 +- 5 分鐘，我認為應該有更快的方法：lst = []for x, y in df.groupby(['VisitorID', 'EpochTime']): lst.append(y.iloc[:1])df_first = pd.concat(lst, ignore_index=True)df_first['HitTime'] = 0.0df_first['HitNumber'] = 0.0df_first['PagePath'] = 'Home'print(df_first) VisitorID EpochTime HitTime HitNumber PagePath0 1000 1521333510 0.0 0.0 Home1 1000 1554888560 0.0 0.0 Homedf_final = pd.concat([df, df_first], ignore_index=True).sort_values(['VisitorID', 'EpochTime', 'HitNumber']).reset_index(drop=True)的輸出df_final是我的預期輸出。所以問題是，我能以更有效的方式做到這一點嗎？
1 回答
海綿寶寶撒
TA貢獻1809條經驗 獲得超8個贊
您可以DataFrame.drop_duplicates稍微提高性能：
d = {'HitTime':0,'HitNumber':0,'PagePath':'Home'}
df_first = df.drop_duplicates(['VisitorID', 'EpochTime']).assign(**d)
df_final = (pd.concat([df, df_first], ignore_index=True)
.sort_values(['VisitorID', 'EpochTime', 'HitNumber'])
.reset_index(drop=True))
print(df_final)
VisitorID EpochTime HitTime HitNumber PagePath
0 1000 1521333510 0 0 Home
1 1000 1521333510 990 14 orders/details
2 1000 1521333510 4149 29 orders/payment
3 1000 1521333510 6450 54 orders/myorders
4 1000 1554888560 0 0 Home
5 1000 1554888560 1400 23 orders/details
6 1000 1554888560 5340 54 orders/payment
7 1000 1554888560 7034 55 orders/afterpayment
8 1000 1554888560 11034 65 orders/myorders
9 1000 1554888560 13059 110 customercare
另一個想法是df_first通過減去并按索引最后排序來更改索引值：
d = {'HitTime':0,'HitNumber':0,'PagePath':'Home'}
df_first = df.drop_duplicates(['VisitorID', 'EpochTime']).assign(**d)
df_first.index -= .5
df_final = pd.concat([df, df_first]).sort_index().reset_index(drop=True)
print(df_final)
VisitorID EpochTime HitTime HitNumber PagePath
0 1000 1554888560 0 0 Home
1 1000 1554888560 1400 23 orders/details
2 1000 1554888560 5340 54 orders/payment
3 1000 1554888560 7034 55 orders/afterpayment
4 1000 1554888560 11034 65 orders/myorders
5 1000 1554888560 13059 110 customercare
6 1000 1521333510 0 0 Home
7 1000 1521333510 990 14 orders/details
8 1000 1521333510 4149 29 orders/payment
9 1000 1521333510 6450 54 orders/myorders
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