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索引錯(cuò)誤：列狀態(tài)已選擇

Python

幕布斯6054654 2023-10-05 17:47:47

有一個(gè)數(shù)據(jù)框數(shù)據(jù)如下 InsuranceId InsuranceStatus Date0 Ins1234 DuePayment 2020-06-07 23:59:43.123456+00:001 Ins1234 Successful 2019-06-07 23:59:43.123456+00:002 Ins1234 Successful 2018-06-07 23:59:43.123456+00:003 Ins5678 DuePayment 2020-07-07 22:59:32.123421+00:004 Ins5678 Successful 2019-07-07 22:59:32.123421+00:005 Ins5678 Successful 2018-07-07 22:59:32.123421+00:00嘗試根據(jù) InsuranceId 和 max(Date) 分組創(chuàng)建行號(hào)/排名df['RowNum'] = df.groupby('InsuranceId')['InsuranceStatus']['Date'].rank(method="first", ascending=True)and df['RowNum'] = df.groupby(by=['InsuranceId'])['InsuranceStatus']['Date'].transform(lambda x: x.rank())通過引用PANDAS 中類似 SQL 的窗口函數(shù)：Python Pandas Dataframe 中的行編號(hào)Error: Index Error: Columns status already selected 試圖達(dá)到以下輸出 InsuranceId InsuranceStatus Date RowNum0 Ins1234 DuePayment 2020-06-07 23:59:43.123456+00:00 11 Ins1234 Successful 2019-06-07 23:59:43.123456+00:00 22 Ins1234 Successful 2018-06-07 23:59:43.123456+00:00 33 Ins5678 DuePayment 2020-07-07 22:59:32.123421+00:00 14 Ins5678 Successful 2019-07-07 22:59:32.123421+00:00 25 Ins5678 Successful 2018-07-07 22:59:32.123421+00:00 3有什么我想補(bǔ)充的嗎？請(qǐng)?zhí)岢鋈魏谓ㄗh最終輸出： InsuranceId InsuranceStatus Date Ins1234 DuePayment 2020-06-07 23:59:43.123456+00:00 Ins5678 DuePayment 2020-07-07 22:59:32.123421+00:00

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1 回答

largeQ

TA貢獻(xiàn)2039條經(jīng)驗(yàn) 獲得超8個(gè)贊

使用rank.?只需傳遞您想要進(jìn)行分組的值并對(duì)需要排名的列進(jìn)行排名即可。

df['Rank']?=?df.groupby(by=['InsuranceId'])['Date'].rank(method='max',ascending=False)
df[df['Rank']==1]

輸出：

InsuranceId?InsuranceStatus??Date????????????????????????????????
Ins1234?????DuePayment???????2020-06-07?23:59:43.123456+00:00????
Ins5678?????DuePayment???????2020-07-07?22:59:32.123421+00:00

反對(duì) 回復(fù) 2023-10-05