使用熊猫根据另一列过滤数据框值

Question

具有包含值的df。

name     last_date                     submission_date

mike  2020-04-10 02:22:22.222   2020-04-01 02:22:22.222
mike  2020-04-10 02:22:22.222   2020-04-08 02:22:22.222
mike  2020-04-10 02:22:22.222   2020-04-16 02:22:22.222

ross  2020-04-16 02:22:22.222   2020-04-18 02:22:22.222
ross  2020-04-16 02:22:22.222   2020-04-19 02:22:22.222
ross  2020-04-16 02:22:22.222   2020-04-20 02:22:22.222
ross  2020-04-16 02:22:22.222   2020-04-15 02:22:22.222

carter 2020-04-22 02:22:22.222   2020-04-28 02:22:22.222
carter 2020-04-22 02:22:22.222   2020-04-15 02:22:22.222
carter 2020-04-22 02:22:22.222   2020-04-19 02:22:22.222
carter 2020-04-22 02:22:22.222   2020-04-21 02:22:22.222

根据last_date过滤值。如果submit_date大于last_date，则排除其值

预期输出：

name     last_date                     submission_date

mike  2020-04-10 02:22:22.222   2020-04-01 02:22:22.222
mike  2020-04-10 02:22:22.222   2020-04-08 02:22:22.222

ross  2020-04-16 02:22:22.222   2020-04-15 02:22:22.222

carter 2020-04-22 02:22:22.222   2020-04-15 02:22:22.222
carter 2020-04-22 02:22:22.222   2020-04-19 02:22:22.222
carter 2020-04-22 02:22:22.222   2020-04-21 02:22:22.222

Answer 1

您可以query submission_date小于或等于last_date的数据帧，这将返回满足条件的行，并过滤掉其余的行：

df.query("last_date>=submission_date")

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    name                 last_date         submission_date
0   mike   2020-04-10 02:22:22.222 2020-04-01 02:22:22.222
1   mike   2020-04-10 02:22:22.222 2020-04-08 02:22:22.222
2   ross   2020-04-16 02:22:22.222 2020-04-15 02:22:22.222
3  carter  2020-04-22 02:22:22.222 2020-04-15 02:22:22.222
4  carter  2020-04-22 02:22:22.222 2020-04-19 02:22:22.222
5  carter  2020-04-22 02:22:22.222 2020-04-21 02:22:22.222