pandas 将 NaN 替换为 None 表现出违反直觉的行为 [英] pandas replace NaN to None exhibits counterintuitive behaviour

问题描述

给定一个系列

s = pd.Series([1.1, 1.2, np.nan])
s
0    1.1
1    1.2
2    NaN
dtype: float64

如果需要将 NaN 转换为 None(例如，使用镶木地板)，那么我想要

If the need arises to convert the NaNs to None (to, for example, work with parquets), then I would like to have

0     1.1
1     1.2
2    None
dtype: object

我认为 Series.replace 将是这样做的明显方法，但这里是函数返回的内容:

I would assume Series.replace would be the obvious way of doing this, but here's what the function returns:

s.replace(np.nan, None)

0    1.1
1    1.2
2    1.2
dtype: float64

NaN 被向前填充，而不是被替换.通过 docs，我看到如果第二个参数是 None，那么第一个参数应该是一个字典.基于此，我希望 replace 要么按预期替换，要么抛出异常.

The NaN was forward filled, instead of being replaced. Going through the docs, I see that if the second argument is None, then the first argument should be a dictionary. Based on this, I would expect replace to either replace as intended, or throw an exception.

我相信这里的解决方法是

I believe the workaround here is

pd.Series([x if pd.notna(x) else None for x in s], dtype=object) 
0     1.1
1     1.2
2    None
dtype: object

这很好.但我想了解为什么会发生这种行为，是否记录在案，或者只是一个错误，我必须清除我的 git 配置文件并在问题跟踪器上记录一个......有什么想法吗?

Which is fine. But I would like to understand why this behaviour occurs, whether it is documented, or if it is just a bug and I have to dust off my git profile and log one on the issue tracker... any ideas?

推荐答案

这是行为在 method 参数的文档中:

This is behaviour is in the documentation of the method parameter:

method : {‘pad’, ‘ffill’, ‘bfill’, None}

The method to use when for replacement, when to_replace is a scalar, list or tuple and value is None.

所以在你的例子中 to_replace 是一个 scalar，而 value 是 None.该方法默认为 pad，来自 填充:

So in your example to_replace is a scalar, and value is None. The method by default is pad, from the documentation of fillna:

pad / ffill: propagate last valid observation forward to next valid

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