pandas groupby cummax 只是分配原始值而不是更新 max-so-far
我有这个数据框：
type run corrected_episode Reward
0 notsweet 0 0 35.0
1 notsweet 0 100 20.0
2 notsweet 0 200 20.0
3 notsweet 0 300 22.0
4 notsweet 0 400 20.0
我想创建一个新列 best_so_far，它具有按 type 分组的相应 Reward 单调递增值， 运行和Corrected_episode。很容易，对吧？除了当我尝试使用 groupby 和 cummax 时发生以下情况：
foo['best_so_far'] = foo.groupby(['type','run', ' Corrected_episode']).Reward.cummax() 产量：
type run corrected_episode Reward best_so_far
0 notsweet 0 0 35.0 35.0
1 notsweet 0 100 20.0 20.0
2 notsweet 0 200 20.0 20.0
3 notsweet 0 300 22.0 22.0
4 notsweet 0 400 20.0 20.0
“迄今为止最好的”，嗯，并不是最好的。如果我使用 foo['best_so_far'] = foo.groupby(['type','run',' Corrected_episode']).Reward.apply(lambda x: x.cummax()) 我会得到相同的结果
我知道这是可能的，因为我已经用其他数据帧完成了数十次，但这个简单的过程不起作用，这有点奇怪。
I have this dataframe:
type run corrected_episode Reward
0 notsweet 0 0 35.0
1 notsweet 0 100 20.0
2 notsweet 0 200 20.0
3 notsweet 0 300 22.0
4 notsweet 0 400 20.0
I want to create a new column, best_so_far, that has a monotonically increasing value for the corresponding Reward grouped by type, run, and corrected_episode. Easy enough, right? Except the following happens when I try to use groupby and cummax:
foo['best_so_far'] = foo.groupby(['type','run','corrected_episode']).Reward.cummax() yields:
type run corrected_episode Reward best_so_far
0 notsweet 0 0 35.0 35.0
1 notsweet 0 100 20.0 20.0
2 notsweet 0 200 20.0 20.0
3 notsweet 0 300 22.0 22.0
4 notsweet 0 400 20.0 20.0
The "best so far", well, isn't the best. I get the same results if I use foo['best_so_far'] = foo.groupby(['type','run','corrected_episode']).Reward.apply(lambda x: x.cummax())
I know this is possible because I've done this dozens of times with other dataframes, there's just something weird about this one that this simple procedure just doesn't work.
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您可以尝试删除
Corrected_episodeYou can try remove
corrected_episode发布此内容后，我当然发现了发生了什么，但我将在这里分享我为解决此问题所做的工作，因为这是熊猫容易违反的最小惊讶原则。
解决方案是这样做：
foo['best_so_far'] = foo.groupby(['type','run']).Reward.cummax()也就是说，我过度指定了包含
Corrected_episode 的列，仅针对该元素执行cummax() 会产生意想不到的效果。但是，我最初包含了Corrected_episode 以确保行的顺序正确 - 即，数据帧实际上是处理大量数据的结果（您是看到一个很小的子集），并且数据的顺序对于 cummax()来说不一定是正常的，可以按照我的设想工作。After posting this of course I discovered what happened, but I'm going to share what I did to fix this here because this is the kind of Violation of the Principle of Least Astonishment that pandas is prone to.
The solution was to do this, instead:
foo['best_so_far'] = foo.groupby(['type','run']).Reward.cummax()That is, I over specified the columns by including
corrected_episodethat had the unintended effect of just executingcummax()for that one element. However, I had originally includedcorrected_episodeto ensure that the order of the rows was correct -- i.e., the dataframe was actually the result of massaging a lot of data (you are seeing a teeny tiny subset), and the order of the data wasn't necessarily sane for thecummax()to work as I envisioned.