我有一个包含三列的数据框：
header {
background-color: #fff;
text-align: center;
width: 100%;
z-index: 2;
}
nav {
background-color: #eee;
position: sticky;
top: 0;
width: 100%;
z-index: 2;
}
main {
max-width: 80ch;
margin: auto;
}
article {
margin-top: 40px;
padding: 20px;
background-color: #fff;
}
.flexRow {
display: flex;
flex-wrap: wrap;
flex-direction: row;
justify-content: center;
}
body {
margin: 0;
background-image: 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);
}对于每个ID，如果状态为“重新打开”，则需要获取显示基于日期的第一次“重新打开”的行。所以我的输出看起来像：
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<h2>This is a post</h2>
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</main>
</body>我尝试过：
ID Date Status
0 1 1/1/2000 Complete
1 1 1/4/2000 ReOpened
2 1 1/10/2000 ReOpened
3 1 1/11/2000 Closed
4 1 1/15/2000 ReOpened
5 2 1/2/2000 ReOpened
6 2 1/4/2000 ReOpened
7 2 1/10/2000 Closed
8 3 1/20/2000 Closed
9 3 1/22/2000 Closed
10 4 1/25/2000 ReOpened
，但这不起作用。
答案 0 :(得分：1)
使用groupby和cumsum遮罩进行此操作：
df[df['Status'].eq('ReOpened').groupby(df['ID']).cumsum() == 1]
ID Date Status
1 1 1/4/2000 ReOpened
5 2 1/2/2000 ReOpened
10 4 1/25/2000 ReOpened
您还可以在过滤后使用groupby和first仅获得第一行：
df[df['Status'].eq('ReOpened')].groupby('ID', as_index=False).first()
ID Date Status
0 1 1/4/2000 ReOpened
1 2 1/2/2000 ReOpened
2 4 1/25/2000 ReOpened
如果性能很重要，则可以使用eq和duplicated将以上内容简化为单个布尔索引操作：
df[df['Status'].eq('ReOpened') & ~df.duplicated(['ID', 'Status'])]
ID Date Status
1 1 1/4/2000 ReOpened
5 2 1/2/2000 ReOpened
10 4 1/25/2000 ReOpened
答案 1 :(得分：1)
drop_duplicates应该足够了。
df[df.Status.eq('ReOpened')].drop_duplicates(['ID'])
# ID Date Status
#1 1 1/4/2000 ReOpened
#5 2 1/2/2000 ReOpened
#10 4 1/25/2000 ReOpened