根据多个条件分配值
[英]Assign values based on multiple conditions
问题描述
My dataframe looks like this:
我的数据框如下所示：
timestamp price amount amount_f status eth_amount
0 2018-11-30 13:48:00 0.00348016 10 0 cancelled 0.000000
1 2018-11-30 13:48:00 0.00350065 10 0 cancelled 0.000000
2 2018-11-30 13:50:00 0.00348021 10 0 cancelled 0.000000
3 2018-11-30 13:50:00 0.00350064 10 0 cancelled 0.000000
4 2018-11-30 13:51:00 0.00348054 10 0 cancelled 0.000000
5 2018-11-30 13:51:00 0.00349873 10 0 cancelled 0.000000
6 2018-11-30 13:52:00 0.00348094 10 10 filled 0.034809
7 2018-11-30 13:52:00 0.00349692 10 0 cancelled 0.000000
What I would need in fact is to modify the colmun status based on the values of the column amount and amount_f .我实际上需要的是根据列amount和amount_f的值修改 colmun 状态。 It would be an if statement as follow:这将是一个 if 语句，如下所示：
df.amount == df.amount_f then df.status = filled
elif df.amount_f == 0 then df.status = cancelled
else df.status = partially_filled
How could I go about this?我该怎么办？
1 个解决方案
解决方案1
5 已采纳 2019-02-07 15:56:44
You can use np.select for this, which allows you to select from a list of values ( choicelist ) depending on the results of a list of conditions:您可以为此使用np.select ，它允许您根据条件列表的结果从值列表 ( choicelist ) 中进行选择：
c1 = df.amount == df.amount_f
c2 = df.amount_f == 0
df.loc[:, 'status'] = np.select(condlist=[c1, c2],
choicelist=['filled', 'cancelled'],
default='partially_filled')
timestamp price amount amount_f status eth_amount
0 2018-11-30 13:48:00 0.003480 10 0 cancelled 0.000000
1 2018-11-30 13:48:00 0.003501 10 0 cancelled 0.000000
2 2018-11-30 13:50:00 0.003480 10 0 cancelled 0.000000
3 2018-11-30 13:50:00 0.003501 10 0 cancelled 0.000000
4 2018-11-30 13:51:00 0.003481 10 0 cancelled 0.000000
5 2018-11-30 13:51:00 0.003499 10 0 cancelled 0.000000
6 2018-11-30 13:52:00 0.003481 10 10 filled 0.034809
7 2018-11-30 13:52:00 0.003497 10 0 cancelled 0.000000
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