根据自定义条件从csv中获取最新行

Question

我有一个表material

+--------+-----+-------------------+----------------+-----------+          
| ID     | REV | name              | Description    | curr      |
+--------+-----+-------------------+----------------+-----------+
| 211-32 | 001 | Screw 1.0         | Used in MAT 1  | READY     |
| 211-32 | 002 | Screw 2 plus      | can be Used-32 | WITHDRAWN |
| 212-41 | 001 | Bolt H1           | Light solid    | READY     |
| 212-41 | 002 | BOLT H2+Form      | Heavy solid    | READY     |
| 101-24 | 001 | HexHead 1-A       | NOR-1          | READY     |
| 101-24 | 002 | HexHead Spl       | NOR-22         | READY     |
| 423-98 | 001 | Nut Repair spare  | NORM1          | READY     |
| 423-98 | 002 | Nut Repair Part-C | NORM2          | WITHDRAWN |
| 423-98 | 003 | Nut SP-C          | NORM2+NORM1    | NULL      |
| 654-01 | 001 | Bar               | Specific only  | WITHDRAWN |
| 654-01 | 002 | Bar rod-S         | Designed+Spe   | WITHDRAWN |
| 654-01 | 003 | Bar OPG           | Hard spec      | NULL      |
+--------+-----+-------------------+----------------+-----------+

在这里，每个ID可以有多个修订。我想采用最新修订(即001002003等的最高值(。但如果最新修订的curr为NULL(字符串(或WITHDRAWN，则我必须采用以前的修订及其相应值。即使是curr也是NULL或WITHDRAWN，我也必须再次进行上一次修订。如果所有的修订都有相同的问题，那么我们可以忽略它。所以预期的输出是

+--------+-----+------------------+---------------+-------+
| ID     | REV | name             | Description   | curr  |
+--------+-----+------------------+---------------+-------+
| 211-32 | 001 | Screw 1.0        | Used in MAT 1 | READY |
| 212-41 | 002 | BOLT H2+Form     | Heavy solid   | READY |
| 101-24 | 002 | HexHead Spl      | NOR-22        | READY |
| 423-98 | 001 | Nut Repair spare | NORM1         | READY |
+--------+-----+------------------+---------------+-------+

我对Python很陌生。我已经尝试了以下代码，但我没有工作。任何建议都将不胜感激。

import pandas as pd
import numpy as np
mydata = pd.read_csv('C:/Myfolder/Python/myfile.csv')
mydata.sort_values(['ID','REV'], ascending=[True, False]).drop_duplicates('',keep=last)

Answer 1

我们可以创建一个psuedo列来获取最大值并返回其索引。

第一步是过滤掉我们想要忽略的值。

df1 = df.loc[
df[~df["curr"].isin(["WITHDRAWN", "NULL"])]
.assign(key=df["REV"].astype(int))
.groupby("ID")["key"]
.idxmax()
]

ID  REV                 name       Description   curr
6   101-24   002   HexHead Spl          NOR-22           READY
1   211-32   001   Screw 1.0            Used in MAT 1    READY
4   212-41   002   BOLT H2+Form         Heavy solid      READY
7   423-98   001   Nut Repair spare     NORM1            READY

Answer 2

您可以使用isin选择其中没有NULL或WITHDRAW的行，然后执行sort_values和drop_duplicates:

mydata = mydata[~mydata['curr'].isin(['NULL','WITHDRAW'])]
mydata = mydata.sort_values(['ID','REV']).drop_duplicates('ID',keep='last')

Answer 3

我认为首先应该从表中删除NULL或WITHDRAW。

mydata[mydata[curr] == 'Ready']       # this should do I think...

然后你可以尝试你的排序，取最大转速值。

mydata = mydata.sort_values(['ID','REV']).drop_duplicates('ID',keep='last')