在 pandas 中用布尔数组索引对象的最惯用的方法是什么? [英] What is the most idiomatic way to index an object with a boolean array in pandas?

问题描述

我正在特别谈论Pandas版本0.11，因为我正忙于用.loc或.iloc替换.ix的用法.我喜欢这样的事实，区分.loc和.iloc可以传达我是否打算按标签或整数位置进行索引.我看到任何一个都可以接受布尔数组，但是我想保持纯洁的用法以清楚地表达我的意图.

I am particularly talking about Pandas version 0.11 as I am busy replacing my uses of .ix with either .loc or .iloc. I like the fact that differentiating between .loc and .iloc communicates whether I am intending to index by label or integer position. I see that either one will accept a boolean array as well but I would like to keep their usage pure to clearly communicate my intent.

推荐答案

在11.0中，这三种方法都可以使用

In 11.0 all three methods work, the way suggested in the docs is simply to use df[mask]. However, this is not done on position, but purely using labels, so in my opinion loc best describes what's actually going on.

更新:我在 github 上问过，结论是df.iloc[msk]将在熊猫11.1中给出NotImplementedError(如果是整数索引掩码)或ValueError(如果是非整数索引).

Update: I asked on github about this, the conclusion being that df.iloc[msk] will give a NotImplementedError (if integer indexed mask) or ValueError (if non-integer indexed) in pandas 11.1.

In [1]: df = pd.DataFrame(range(5), list('ABCDE'), columns=['a'])

In [2]: mask = (df.a%2 == 0)

In [3]: mask
Out[3]:
A     True
B    False
C     True
D    False
E     True
Name: a, dtype: bool

In [4]: df[mask]
Out[4]:
   a
A  0
C  2
E  4

In [5]: df.loc[mask]
Out[5]:
   a
A  0
C  2
E  4

In [6]: df.iloc[mask]  # Due to this question, this will give a ValueError (in 11.1)
Out[6]:
   a
A  0
C  2
E  4

也许值得一提的是，如果给掩码整数索引，它将引发错误:

Perhaps worth noting that if you gave mask integer index it would throw an error:

mask.index = range(5)
df.iloc[mask]  # or any of the others
IndexingError: Unalignable boolean Series key provided

这表明iloc实际上并未实现，它使用标签，因此为什么当我们尝试使用11.1时会抛出NotImplementedError.

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